Waveform enhanced reflection and margin boundary characterization for ultrasound tomography

ABSTRACT

The present invention provides improved methods and systems for generating enhanced images of a volume of tissue. In an aspect, the method comprises receiving from a transducer, a plurality of acoustic signals derived from acoustic waveforms transmitted through the volume of tissue; generating from the acoustic signals, a first reflection rendering that characterizes sound reflection, the first reflection rendering comprising a first distribution of reflection values across a region of the volume of tissue; generating from the acoustic signals, a sound speed rendering that characterizes sound speed, the sound speed rendering comprising a distribution of sound speed values across the region; generating from the sound speed rendering, a second reflection rendering that characterizes sound reflection, the second reflection rendering comprising a second distribution of reflection values across the region; and rendering one or more combined images, based on the first reflection rendering and the second reflection rendering.

CROSS-REFERENCE

This application continuation application of Ser. No. 15/829,748(Attorney Docket No. 50174-735.201), filed Dec. 1, 2017, which claimsthe benefit of provisional patent application U.S. Prov. Ser. App. No.62/429,542 (Attorney Docket No. 50174-735.101), filed Dec. 2, 2016,entitled “Waveform Enhanced Reflection for Ultrasound Tomography”, andU.S. Prov. Ser. App. No. 62/429,563 (Attorney Docket No. 50174-736.101),filed Dec. 2, 2016, entitled “Margin Boundary Characterization andRegion of Interest Diagnostics for Ultrasound Tomography”, which areeach incorporated herein by reference in their entirety.

The subject matter of this application is related to the followingpatent applications: U.S. patent application Ser. No. 14/817,470entitled “Method for Generating an Enhanced Image of A Volume of Tissue”(Attorney Docket No. 50174-725.201); U.S. patent application Ser. No.14/819,091 entitled “Ultrasound Waveform Tomography Method and System”(Attorney Docket No. 50174-728.201); and P.C.T. International App. Pub.No. WO2017040866 entitled “Tissue Imaging and Analysis Using UltrasoundWaveform Tomography” (Attorney Docket No. 50174-732.601), which are eachincorporated herein by reference in their entirety.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with the support of the United States governmentunder Grant number R44 CA165320 by National Cancer Institute. TheGovernment has certain rights in the invention.

BACKGROUND OF THE INVENTION

Current methods for ultrasound reflection imaging can be less than idealin some respects. For example, current methods for ultrasound reflectionimaging may be based on the coherent summation of pulse echo signals.While methods based on a pulse echo approach work well in breastsdominated by fatty tissues, there may be limitations when imaging densebreasts. These limitations may arise from (i) loss of signal coherencywhen illuminating large tissue volumes (e.g., patient motion), (ii) lossof coherency arising from multiple scattering events in dense breasttissue, (model mismatch) and/or (iii) inability to penetrate deep intodense highly attenuating tissue.

Conventional techniques for imaging and diagnosing breast and othertypes of cancer such as, for example, mammography, magnetic resonanceimaging (MRI), or ultrasound can be less than ideal in at least somerespects. For example, MRI can be prohibitively expensive for routineuse. In another example, mammography involves ionizing radiation, whichmay limit frequency of patient screening, and may lack specificity inidentifying various types of masses such as, for example, more deadly,invasive carcinomas and/or less deadly Ductal Carcinomas in situ. Such alack of specificity may result in excessive patient biopsies and/orinitial misdiagnoses. Additionally, mammography may have low sensitivityin patients with dense breast tissue resulting in missed deadly invasivecancers. In an additional example, the efficacy of conventionalultrasound techniques in imaging and diagnosing cancer may be limited bythe skill of the operator. Additionally, conventional ultrasound may notbe optimally configured or employed to image a whole organ, withsufficient resolution and/or image quality to sufficiently differentiatevarious types of tissue or to correctly give a diagnosis.

In light of the above, imaging and diagnostic methods which improvequality of tissue imaging and specificity of mass characterization maybe needed. Ideally, such methods may provide enhanced image quality(e.g., with high resolution) through an imaging modality that isinexpensive and does not use ionizing radiation (e.g., ultrasoundimaging), thereby enabling accurate identification and classification ofvarious types of breast masses with sufficient clinical sensitivity andspecificity.

SUMMARY OF THE INVENTION

Recognizing a need for high-quality imaging of tissue, the presentdisclosure provides improved methods and systems for enhancing areflection image of a volume of tissue. Conventional reflection imaging,such as envelope detection (ERF) imaging, provides echo contrast fordetection and characterization of lesions. Another method, REF imaging(raw RF signals), helps define margins and cancer-specific spiculationsand architectural distortion. Embodiments of the present disclosureprovide the use of waveform reconstruction which may leverage thebenefits of both ERF imaging and REF imaging, while mitigating thelimitations of conventional methods noted above. Such mitigation may bemade possible by waveform's ability to account for multiple scatters andprovide deep penetration (by virtue of the lower frequencies used) whilemaintaining high spatial resolution.

The methods described herein, by which waveform data can be added topulse-echo data, may utilize acoustic impedance information gatheredfrom sound speed images. Since changes in acoustic impedance may yieldreflections, the method may use the gradient of the sound speed image tocalculate reflection information. The low frequencies (˜1 MHz)associated with the sound speed waveform reconstructions may provideinformation on specular reflections (down to ˜1 mm). Since pulse echoimaging may occur at higher frequencies (˜1 to 5 MHz), the latter may bebetter able to image the sub-mm granularity that provides information onspeckle patterns.

Since RF signals may represent the native reflections (highlightingtissue edges) and may provide the most flexibility for furthermanipulation and conditioning, the methods disclosed herein provide ameans to combine the contribution from the REF images with the waveformcomponent. Such a method may preserve high spatial resolution andsensitivity to edges while additionally preserving the generalappearance of a reflection image. Additionally, the methods disclosedherein may sufficiently improve image quality for accurate massdetection, and encourage mass discrimination to avoid unnecessarypatient biopsies.

An aspect of the present disclosure provides improved methods forhigh-specificity characterization of breast tissue based on classifiermodels which identify a region of interest and develop prognosticparameters, which prognostic parameters comprise measurements of soundpropagation interior to and exterior to the region of interest,qualitative assessments of the tumor morphology (e.g., the shape of theboundary of the region of interest), and semi-quantitative parameters(e.g., a score on an integer scale of the margin boundary). The methodsdisclosed herein may sufficiently improve specificity of masscharacterization, which methods encourage mass discrimination to avoidexcessive patient biopsies.

Another aspect of the present disclosure provides a method forgenerating an enhanced image of a volume of tissue, which method isimplemented by a computer comprising one or more processors and computerreadable media comprising instructions. The method may comprise:receiving from a transducer, a plurality of acoustic signals derivedfrom acoustic waveforms transmitted through the volume of tissue,wherein the transducer comprises an array of ultrasound transmitters andan array of ultrasound receivers configured to surround the volume oftissue; generating from the plurality of acoustic signals, a firstreflection rendering that characterizes sound reflection, the firstreflection rendering comprising a first distribution of reflectionvalues across a region of the volume of tissue; generating from theplurality of acoustic signals, a sound speed rendering thatcharacterizes sound speed, the sound speed rendering comprising adistribution of sound speed values across the region of the volume oftissue; generating from the sound speed rendering, a second reflectionrendering that characterizes sound reflection, the second reflectionrendering comprising a second distribution of reflection values acrossthe region of the volume of tissue; and rendering one or more combinedimages, in response to the first reflection rendering and the secondreflection rendering, thereby generating the enhanced image of thevolume of tissue.

In some embodiments, the enhancement comprises an increase in resolutionof the image from greater than about 1 mm to less than about 0.7 mm. Insome embodiments, the enhancement comprises an increase in contrast ofthe image from a factor of at least about 2 to a factor of at leastabout 10. In some embodiments, the enhancement comprises an increase inconspicuity of lesions in the volume of tissue from a factor of at leastabout 2 to a factor of at least about 10. In some embodiments, theenhancement comprises an increase in specificity of lesioncharacterization in the volume of tissue such that the specificity is atleast about 75% to at least about 95%. In some embodiments, the tissuecomprises breast tissue.

In some embodiments, the volume of tissue comprises a distribution ofone or more of: fat tissue, parenchymal tissue, cancerous tissue, andabnormal tissue. In some embodiments, the fat tissue comprises fattyparenchyma, parenchymal fat, or subcutaneous fat. In some embodiments,the abnormal tissue comprises fibrocystic tissue or a fibroadenoma. Insome embodiments, generating the first reflection rendering comprisesgenerating a plurality of acoustomechanical parameter slices associatedwith a plurality of coronal slices through the volume of tissue. In someembodiments, generating the first reflection rendering comprisesgenerating a distribution of acoustic reflection signals, wherein theacoustic reflection signals characterize a relationship betweenreflected intensities and emitted intensities of the acoustic waveforms,wherein the relationship is selected from the group consisting of a sum,a difference, and a ratio. In some embodiments, generating the firstreflection rendering comprises generating a distribution of acousticreflection signals, wherein the acoustic reflection signals characterizea change in acoustic impedance of the volume of tissue. In someembodiments, generating the first reflection rendering comprisesgenerating a distribution of acoustic reflection signals received from afirst array of transducers, the first array of transducers transmittingand receiving at a first frequency greater than a second frequency of asecond array of transducers used to generate the sound speed rendering.In some embodiments, generating the first reflection rendering comprisesgenerating a distribution of acoustic reflection signals received from afirst array of transducers, the first array of transducers transmittingand receiving at a first frequency less than a second frequency of asecond array of transducers used to generate the sound speed rendering.In some embodiments, generating the sound speed rendering comprisesgenerating a plurality of acoustomechanical parameter slices associatedwith a plurality of coronal slices through the volume of tissue. In someembodiments, the sound speed rendering comprises a real portioncorresponding to a phase velocity, and an imaginary portioncorresponding to a sound attenuation.

In some embodiments, generating the sound speed rendering comprisesgenerating an initial sound speed rendering based on simulated waveformsaccording to a time travel tomography algorithm, and the initial soundspeed rendering is iteratively optimized until ray artifacts are reducedto a pre-determined threshold. In some embodiments, the simulatedwaveforms are optimized for each of a plurality of sound frequencycomponents. In some embodiments, generating the second reflectionrendering comprises calculating a gradient of the sound speed rendering.In some embodiments, calculating the gradient comprises performing oneor more algorithms selected from the group consisting of theSobel-Feldman operator, the Scharr operator, the Prewitt operator, andthe Roberts Cross operator. In some embodiments, calculating generatinga second reflection rendering comprises performing computationalrelations selected from the group consisting of averaging, truncation,normalization, smoothing, addition, subtraction, multiplication, anddivision. In some embodiments, smoothing comprises convolution withanother function, adjacent averaging, or Fourier filtering.

In some embodiments, rendering the one or more combined images comprisesperforming an element-wise average or a weighted average of the firstreflection rendering and the second reflection rendering. In someembodiments, rendering the one or more combined images comprisesperforming an element-wise sum or a weighted sum of the first reflectionrendering and the second reflection rendering. In some embodiments,rendering the one or more combined images comprises performing anelement-wise product or a weighted product of the first reflectionrendering and the second reflection rendering. In some embodiments,rendering the one or more combined images comprises performing aconvolution of the first reflection rendering and the second reflectionrendering. In some embodiments, rendering the one or more combinedimages comprises performing computational relations on the firstreflection rendering and/or the second reflection rendering, thecomputational relations selected from the group consisting of averaging,truncation, normalization, smoothing, addition, subtraction,multiplication, and division.

In some embodiments, the method further comprises classifying, based onthe one or more combined images, different types of lesions in thevolume of tissue as at least one of a cancerous tumor, a fibroadenoma, acyst, a nonspecific benign mass, and an unidentifiable mass.

In some embodiments, the present disclosure provides a non-transitorycomputer-readable storage medium with instructions stored thereon that,when executed by a processor, cause a processor to perform the disclosedmethod.

In some embodiments, the present disclosure provides a system forgenerating enhanced images of a volume of tissue. The system maycomprise: a transducer array comprising an array of ultrasound emittersand an array of ultrasound receivers, the transducer array configured tosurround a volume of tissue, wherein the array of ultrasoundtransmitters is configured to emit acoustic waveforms toward the volumeof tissue, wherein the array of ultrasound receivers is configured toreceive the emitted acoustic waveforms and convert the received acousticwaveforms to a plurality of acoustic signals; a processor comprising acomputer readable medium configured with instructions, that whenexecuted, cause the processor to perform the disclosed method; and adisplay visible to a user configured to display the one or more combinedimages.

Another aspect of the present disclosure provides a method forcharacterizing a volume of breast tissue of a patient. The method maycomprise: receiving from a transducer, a plurality of acoustic signalsderived from acoustic waveforms transmitted through the volume oftissue, wherein the transducer comprises an array of ultrasoundtransmitters and an array of ultrasound receivers configured to surroundthe volume of tissue; generating from the plurality of acoustic signals,a three-dimensional acoustic rendering that characterizes soundpropagation within the volume of tissue; receiving input from a usercorresponding to a user-selected region of interest; generating from theacoustic rendering a first plurality of prognostic parameterscorresponding to sound propagation interior to the user-selected regionof interest; generating from the acoustic rendering a second pluralityof prognostic parameters corresponding to sound propagation exterior tothe user-selected region of interest; and characterizing a lesion withinthe volume of tissue using the first plurality of interior prognosticparameters and the second plurality of exterior prognostic parameters.

In some embodiments, the three-dimensional acoustic rendering comprisesan acoustic attenuation rendering, an acoustic reflection rendering, andan acoustic sound speed rendering. In some embodiments, the methodfurther comprises generating a merged three-dimensional rendering of theacoustic reflection rendering, the acoustic attenuation rendering, andthe acoustic sound speed rendering. In some embodiments, the firstplurality of prognostic parameters comprises a volume-average value ofacoustic attenuation, a volume-average value of acoustic speed, and avolume-average value of acoustic reflectivity. In some embodiments, thesecond plurality of prognostic parameters comprises a volume-averagevalue of acoustic attenuation, a volume-average value of acoustic speed,and a volume-average value of acoustic reflectivity. In someembodiments, the user-selected region of interest is selected from atleast one of an acoustic attenuation rendering, an acoustic reflectionrendering, and an acoustic sound speed rendering. In some embodiments,the user-selected region of interest is selected from the merged threedimensional rendering. In some embodiments, selection of theuser-selected region of interest is aided or optimized by acomputer-implemented algorithm.

In some embodiments, a lesion in the volume of tissue is classified asat least one of a cancerous tumor, a fibroadenoma, a cyst, a nonspecificbenign mass, and an unidentifiable mass. In some embodiments,classifying a lesion in the volume of tissue comprises using a thresholdvalue of the first plurality of prognostic parameters or the secondplurality of prognostic parameters. In some embodiments, at least oneprognostic parameter among the first plurality of prognostic parametersor the second plurality of prognostic parameters comprises auser-selected classification of a margin of the region of interestaccording to an integer scale. In some embodiments, at least oneprognostic parameter among the first plurality of prognostic parametersor the second plurality of prognostic parameters comprises a differencebetween an acoustic parameter interior to the region of interest and anacoustic parameter exterior to the region of interest. In someembodiments, the first plurality of prognostic parameters comprises avolume-standard-deviation value of acoustic attenuation, avolume-average value of acoustic speed, and a volume-average value ofacoustic reflectivity. In some embodiments, the first plurality ofprognostic parameters comprises a volume-standard-deviation value ofacoustic attenuation, a volume-average value of acoustic speed, and avolume-average value of acoustic reflectivity. In some embodiments, thespecificity of classification of a lesion in the volume of tissue isincreased such that the specificity is at least about 75% to at leastabout 95%.

In some embodiments, the present disclosure provides a system forgenerating enhanced images of a volume of tissue. The system maycomprise: a transducer array comprising an array of ultrasound emittersand an array of ultrasound receivers, the transducer array configured tosurround a volume of tissue, wherein the array of ultrasoundtransmitters is configured to emit acoustic waveforms toward the volumeof tissue, wherein the array of ultrasound receivers is configured toreceive the emitted acoustic waveforms and convert the received acousticwaveforms to a plurality of acoustic signals; a processor comprising acomputer readable medium configured with instructions, that whenexecuted, cause the processor to perform the disclosed method; and adisplay visible to a user configured to display the one or more combinedimages.

Another aspect of the present disclosure provides a method of breastultrasound imaging and analysis. The method may comprise: scanning abreast with ultrasound tomography; determining a tumor sound speed andsound reflection based on the scanning; calculating a gradient of thetumor sound speed; determining a tumor sound reflectivity based on thegradient and a response to a radio frequency component of the soundreflection; and outputting an image of the breast based on the tumorsound reflectivity.

Another aspect of the present disclosure provides a method of breastultrasound imaging and analysis. The method may comprise: scanning abreast with ultrasound tomography; determining a tumor sound speed,sound reflection, and sound attenuation based on the scanning;generating a reflection score based on the tumor sound speed andattenuation; characterizing mass margins of the breast based on thereflection score; and identifying a tumoral or peritumoral region ofinterest within the breast.

Another aspect of the present disclosure provides a non-transitorycomputer readable medium comprising machine-executable code that, uponexecution by one or more computer processors, implements any of themethods above or elsewhere herein.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 shows a method of generating an enhanced image of a volume oftissue, in accordance with embodiments.

FIG. 2A is a schematic of an exemplary ultrasound scanner, in accordancewith embodiments.

FIG. 2B is a schematic of a patient breast in an exemplary ultrasoundscanner, in accordance with embodiments.

FIG. 2C is a schematic of an exemplary ultrasound transducer of anultrasound scanner, in accordance with embodiments.

FIG. 3 is a schematic showing the generation of 3D renderings fromstacks of 2D images of three acoustic data types, in accordance withembodiments.

FIG. 4 shows exemplary coronal slices of several three dimensionalrenderings of each of several acoustic data types of the same patientbreast comprising a cancerous lesion at various steps in a method ofgenerating an enhanced image, in accordance with embodiments.

FIG. 5 shows exemplary coronal slices of the same patient breast fromFIG. 4 additionally comprising an enhanced image after fat correction,in accordance with embodiments.

FIG. 6A shows exemplary coronal slices the same patient breastcomprising a cancerous lesion and a malignant node from different typesof acoustic renderings, in accordance with embodiments.

FIG. 6B shows magnified images of the acoustic renderings in FIG. 6A, inaccordance with embodiments.

FIG. 7 shows an exemplary method for characterizing a volume of breasttissue of a patient, in accordance with embodiments.

FIG. 8A shows exemplary regions of interest comprising various marginboundaries with varying boundary morphologies, in accordance withembodiments.

FIG. 8B shows an exemplary ellipsoidal region of interest with a marginboundary and internal and external layers, in accordance withembodiments.

FIG. 9A shows an exemplary classifier model comprising a threshold valueof a single prognostic parameter, in accordance with embodiments.

FIG. 9B shows an exemplary classifier model comprising a threshold valueof an interior prognostic parameter and an exterior prognosticparameter, in accordance with embodiments.

FIG. 9C shows an exemplary classifier model comprising a mixedqualitative and quantitative metric for assessing a mass, in accordancewith embodiments.

FIG. 10A shows a coronal view comparison between UST speed of sound andMR contrast enhanced fat subtracted images of representative breastparenchyma.

FIG. 10B shows profile cuts of thin features using, the full-width,half-maximum criterion to estimate the spatial resolution of eachmodality.

FIG. 11 shows the basic differences in sound speed texture andmorphology noted for many cysts, fibroadenomas and cancer.

FIG. 12A shows box plots summarizing first-order sound reflectionstatistics within the tumoral ROI and comparisons with the surroundingperitumoral region.

FIG. 12B shows box plots summarizing first-order sound speed statisticswithin the tumoral ROI and comparisons with the surrounding peritumoralregion.

FIG. 12C shows box plots summarizing first-order attenuation statisticswithin the tumoral ROI and comparisons with the surrounding peritumoralregion.

FIG. 13A shows a scatter plot based on the mean sound speed of theperitumoral mass region and the standard deviation of the attenuationwithin the tumor.

FIG. 13B shows a scatter plot based on the standard-deviation of thesound speed within the tumor and the standard-deviation of theattenuation within the tumor.

FIG. 14 shows a computer control system that is programmed or otherwiseconfigured to implement methods provided herein.

DETAILED DESCRIPTION OF THE INVENTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich this invention belongs. As used in this specification and theappended claims, the singular forms “a,” “an,” and “the” include pluralreferences unless the context clearly dictates otherwise. Any referenceto “or” herein is intended to encompass “and/or” unless otherwisestated.

In the following detailed description, reference is made to theaccompanying figures, which form a part hereof. In the figures, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, figures, and claims are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the scope of the subject matter presented herein. It willbe readily understood that the aspects of the present disclosure, asgenerally described herein, and illustrated in the figures, can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are explicitlycontemplated herein.

Reference throughout this specification to “one embodiment”, “anembodiment”, or “some embodiments” means that a particular feature,structure, or characteristic described may be included in at least oneembodiment of the present invention, and each of these embodiments maybe combined with other embodiments in accordance with the presentdisclosure. Thus, the appearances of the phrases “in one embodiment”,“in an embodiment”, or “in some embodiments” throughout thisspecification do not necessarily all refer to the same embodiment.Furthermore, the particular features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments.

Embodiments of the present disclosure provide a method for generating anenhanced image of a volume of tissue. The method may be implemented by acomputer comprising one or more processors and computer readable mediacomprising instructions to perform the method for generating an enhancedimage of a volume of tissue. FIG. 1 shows an exemplary method 100 forgenerating an enhanced image of a volume of tissue, in accordance withsome embodiments. The method 100 may comprise emitting toward the volumeof tissue a plurality of acoustic waveforms and detecting from thevolume of tissue a plurality of acoustic signals with a transducer. Thetransducer may comprise an array of ultrasound transmitters and an arrayof ultrasound receivers configured to surround the volume of tissue 110.The method 100 may further comprise receiving from a transducer, aplurality of acoustic signals derived from acoustic waveformstransmitted through the volume of tissue 120. The method 100 may furthercomprise generating from the plurality of acoustic signals, a firstreflection rendering that characterizes sound reflection. The firstreflection rendering may comprise a first distribution of reflectionvalues across a region of the volume of tissue 130. The method 100 mayfurther comprise generating from the plurality of acoustic signals, asound speed rendering that characterizes sound speed. The sound speedrendering may comprise a distribution of sound speed values across theregion of the volume of tissue 140. The method 100 may further comprisegenerating from the sound speed rendering, a second reflection renderingthat characterizes sound reflection. The second reflection rendering maycomprise a second distribution of reflection values across the region ofthe volume of tissue 150. The method 100 may further comprise renderingone or more combined images, based on the first reflection rendering andthe second reflection rendering, thereby generating the enhanced imageof the volume of tissue 160.

In some embodiments, method 100 may function to render ultrasound imagesthat enhance target objects within a field of view, in order tofacilitate characterization of the volume of tissue (e.g., a wholebreast, another organ). Additionally or alternatively, the volume oftissue may comprise a distribution of one or more of: fat tissue (e.g.,fatty parenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymaltissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue,fibroadenomas, etc.), and any other suitable tissue type within thevolume of tissue.

Additionally or alternatively, method 100 may function to enhance theimage resolution of a reflection image by up to 5 times (i.e., 5×), forexample, within a range defined between any two of the following values:about 1.05×, about 1.1×, about 1.2×, about 1.5×, and about 5×.Additionally or alternatively, method 100 may function to enhance theimage contrast of a reflection image by up to 10×, for example, within arange defined between any two of the following values: about 1.05×,about 1.1×, about 2×, about 5×, and about 10×. Additionally oralternatively, method 100 may function to enhance the conspicuity oflesions in a reflection image. Additionally or alternatively, method 100may function to produce images that may be aligned with regulatorystandards for medical imaging, as regulated, for instance, by the U.S.Food and Drug Administration (FDA). Method 100 may be used tocharacterize tissue of a human breast, but may additionally oralternatively be used to characterize tissue of an arm, leg, otherappendage, and/or any suitable volume of tissue in a human or otheranimal. In relation to current ultrasound methods and systems, method100 may improve sensitivity in detection of suspicious masses withinacoustic reflection renderings, while providing specificity incharacterization of types of masses. Such masses may include but are notlimited to: a cancerous tumor, a fibroadenoma, a cyst, a nonspecificbenign mass, and an unidentifiable mass. Method 100 may, however,function to enable diagnosis, monitoring, and/or characterization of avolume of tissue in any other suitable manner.

In some embodiments, method 100 may be used to generate one or morerenderings that may be used to detect abnormalities (e.g., canceroustissues) in a human or other animal. As such, in one variation, method100 may be used to characterize the tissue to facilitate diagnoses ofcancer, assess its type, and determine its extent (e.g., to determinewhether a mass in the tissue may be surgically removable), or to assessrisk of cancer development (e.g., measuring breast tissue density). Inyet another embodiment, method 100 may be used to characterize and/orinvestigate particular aspects of the tissue, such as to determinewhether a mass in the tissue may be a tumor, cyst, fibroadenoma, orother kind of mass. Method 100 may be used in any suitable applicationfor imaging a volume of tissue or other suitable object. Method 100 maybe implemented, at least in part, by way of an embodiment, variation,and/or example of the system 200 described in the section titled“Ultrasound Tomography System” below; however, method 100 mayadditionally or alternatively be implemented using any other suitablesystem.

While FIG. 1 shows a method of generating an enhanced image of a volumeof tissue, in accordance with some embodiments, a person of ordinaryskill in the art will recognize many adaptations and variations.Further, one or more steps shown in FIG. 1 may be deleted or repeated,additional steps can be added, and the steps can be performed in anyorder.

Emitting and Receiving Acoustic Signals

At a step 110 of the method 100, a plurality of acoustic waveforms maybe emitted toward the volume of tissue, and a plurality of acousticsignals may be detected from the volume of tissue with a transducer. Thetransducer may comprise an array of ultrasound transmitters and an arrayof ultrasound receivers configured to surround the volume of tissue. Ata step 120, a plurality of acoustic signals derived from acousticwaveforms transmitted through the volume of tissue may be received by acomputer from a transducer. Steps 110 and 120 function to gatheracoustic data from which renderings of the volume of tissue may bederived in other steps of the method 100. Emitting and detectingacoustic waveforms may comprise surrounding the volume of tissue withthe array of ultrasound transmitters and/or with a ring transducercomprising the array of ultrasound transmitters. The acoustic waveformsmay be characterized by frequencies of approximately 1 MHz,approximately 2 MHz, approximately 3 MHz, approximately 4 MHz,approximately 5 MHz, approximately 6 MHz, approximately 7 MHz,approximately 8 MHz, approximately 9 MHz, approximately 10 MHz,approximately 11 MHz, approximately 12 MHz, approximately 13 MHz,approximately 14 MHz, approximately 15 MHz, approximately 16 MHz,approximately 17 MHz, approximately 18 MHz, approximately 19 MHz,approximately 20 MHz, or any suitable frequency for medical imaging orother applications. The array of transmitters may comprise transducerelements configured to emit at a plurality of frequencies. Additionallyor alternatively, an individual element in a transducer array may beconfigured to emit at one of a plurality of frequencies emitted by thearray, such that the frequency of a transducer element may be optimizedfor a particular type of acoustic rendering.

The detected acoustic signals of step 110 may be derived frominteractions between the emitted acoustic waveforms and the tissue,wherein interactions may comprise one or more of: scattering (e.g.,reflection, refraction, diffraction, diffusion, etc.) and transmissionof the acoustic waves through the tissue. The acoustic signals maytravel along a straight, bent, zig-zag, or curved path, or a path of anysuitable shape as determined by the physics of acoustic wavepropagation. Detecting acoustic signals may comprise surrounding thevolume of tissue with the array of ultrasound receivers and/or with aring transducer comprising the array of ultrasound receivers. Theacoustic signals may be characterized by frequencies of approximately 1MHz, approximately 2 MHz, approximately 3 MHz, approximately 4 MHz,approximately 5 MHz, approximately 6 MHz, approximately 7 MHz,approximately 8 MHz, approximately 9 MHz, approximately 10 MHz,approximately 11 MHz, approximately 12 MHz, approximately 13 MHz,approximately 14 MHz, approximately 15 MHz, approximately 16 MHz,approximately 17 MHz, approximately 18 MHz, approximately 19 MHz,approximately 20 MHz, or any suitable frequency for medical imaging orother applications. The array of receivers may comprise transducerelements configured to emit at a plurality of frequencies. Additionallyor alternatively, an individual transducer in an array may be configuredto receive at one of a plurality of frequencies received by the array,such that the frequency of a transducer element may be optimized for aparticular type of acoustic rendering.

At step 110 of the method 100, emitting acoustic waveforms and detectinga set of acoustic signals may be performed with an ultrasoundtomographic scanner, for example, using methods similar to thosedescribed in U.S. Pat. Nos. 6,385,474; 6,728,567; 8,663,113; 8,876,716;and 9,113,835; and U.S. Publication Nos. 2013/0041261 and 2013/0204136,which are each incorporated by reference in their entirety. However, anysuitable ultrasound device or scanner may be used.

FIG. 2A shows a schematic of an exemplary ultrasound scanner 200, inaccordance with embodiments. The ultrasound scanner may be used to emitacoustic waveforms and detect a set of acoustic signals, for example asdiscussed in FIG. 1. The steps of scanning the tissue and detectingacoustic signals may be performed during a scan of a patient lying proneon their stomach on a scanner table 205 having an opening that providesaccess to the volume of tissue of the patient. The table, which may bemade of a durable, flexible material such as sailcloth, may contour tothe patient's body, thereby increasing scanning access to the axillaregions of the breast and increasing patient comfort. The opening in thetable may allow the breast (or other appendage) to protrude through thetable and be submerged in an imaging tank filled with water or anothersuitable fluid as an acoustic coupling medium that propagates acousticwaves.

FIGS. 2B and 2C show a schematic of a patient breast in an exemplaryultrasound scanner and a schematic of an exemplary ultrasound transducerof an ultrasound scanner, in accordance with embodiments. FIGS. 2B and2C show a ring-shaped transducer 220 with transducer elements 222.Transducer 220 may be located within the imaging tank and encircle orotherwise surround the volume of tissue 206. The transducer elements 222may comprise an array of ultrasound transmitters 224 and/or an array ofultrasound receivers 226. Multiple ultrasound transmitters that directsafe, non-ionizing ultrasound pulses toward the tissue and multipleultrasound receivers 226 that receive and record acoustic signalsscattering from the tissue and/or transmitted through the tissue, may bedistributed around the ring transducer 220. The transducer elements maycomprise those configured to receive or transmit higher frequencyacoustic waveforms and those configured to receive or transmit lowerfrequency acoustic waveforms. In an exemplary configuration, the ringtransducer may be organized such that each ultrasound transmitterelement may be paired with a corresponding ultrasound receiver element,each ultrasound transmitter element may be surrounded by two adjacentultrasound transmitter elements, each ultrasound receiver element may besurrounded by two adjacent ultrasound receiver elements, and thetransducer may be axially symmetric, as in FIG. 2C. In anotherconfiguration, the ultrasound transducer may comprise transducer unitswherein each unit may be configured both to transmit and to receive.

During the scan, the ring transducer 220 may move to image all of thetargeted tissue, such as in an anterior-posterior direction between thechest wall and the nipple region of the breast to acquire an acousticdata set including measurements such as acoustic reflection, acousticattenuation, and sound speed. These data may be acquired at discretescanning steps, or coronal “slices”. The ring transducer 220 may beconfigured to scan step-wise in increments and/or travel continuouslyfrom the chest wall towards the nipple, and/or from the nipple towardsthe chest wall. However, the ring transducer 220 may additionally and/oralternatively receive data regarding any suitable biomechanical propertyof the tissue during the scan, and in any suitable direction.

In some embodiments, the scanner table may comprise an embodiment,variation, or example of the patient interface system described in thereferences incorporated herein and additionally or alternatively in U.S.application Ser. No. 14/208,181, entitled “Patient Interface System”,U.S. application Ser. No. 14/811,316 entitled “System for ProvidingScanning Medium”, or P.C.T. International Pat. App. Pub. No.WO2017139389 entitled “System for Shaping and Positioning a TissueBody”, which are each hereby incorporated by reference in theirentirety. However, steps 110 and/or 120 may additionally oralternatively be implemented using any other suitable patient interfacesystem.

Emitting and detecting in step 110 may be performed in a rapid manner,such as with a data acquisition time of less than approximately 1 secondper “slice”, which may help to avoid motion artifacts in the subsequentmorphology renderings and enables the use of contrast agents. However,any other suitable acquisition time may characterize emitting acousticwaveforms and/or detecting acoustic signals as in step 110. The emittedwaveforms and/or detected signals may additionally or alternatively bebeamformed on a transducing element.

A step 110 of the method 100 may comprise transmitting the plurality ofacoustic signals from a transducer 220 to a computer 210 comprising oneor more processors, computer readable media, and a display visible to auser 290 and additionally or alternatively comprising transmittinginstructions to be received by a computer at a step 120. In otherembodiments, steps 110 and/or 120 of the method 100 may additionally oralternatively comprise sending and/or retrieving acoustic signals from acomputer readable storage medium such as a hard drive or an onlineserver. Furthermore, in relation to detecting acoustic signals, themethod 100 may additionally or alternatively comprise processing the setof acoustic signals according to at least one conditioning algorithm.For instance, for a given transmitter/detector pair of transducers,processing the set of acoustic signals may comprise one or more of:reading and correcting the raw data (detected acoustic signals) for DCvariation; implementing a trapezoidal filter to bandpass usefulfrequencies and cut noise; and implementing any other suitable filter(high pass, low pass, etc.) to filter desired frequencies. Furthersignal processing may additionally or alternatively comprise discardingunusable signal such as “muting” in which recorded signal before thetransmission wavefront and/or and after the longest applicable receivingtime (e.g., “top muting” or “bottom muting”), further noise reductionprocesses, and other suitable signal processing steps. However, anyother suitable conditioning process may additionally or alternatively beused.

Processing of Acoustic Data

At a step 120 of the method 100, a plurality of acoustic signals derivedfrom acoustic waveforms transmitted through the volume of tissue may bereceived by a computer from a transducer. The acoustic signals may beprocessed by a computer as acoustic data. Acoustic data may be used togenerate one or more “stacks” of 2D images corresponding to a series of“slices” of the volume of tissue for each measured acoustomechanicalparameter. Each stack of 2D image data may comprise acoustic dataassociated with a particular parameter or property of the tissue, forexample, any type of acoustic data such as acoustic reflection, acousticsound speed, and acoustic attenuation. The processor 210 mayadditionally or alternatively generate a three-dimensional volumetricrendering based on the stack of two-dimensional images, and/or generatea three-dimensional volumetric rendering directly based on the receivedacoustic data. An image representation of any portion of the volume oftissue may depict any one or more acoustomechanical properties of thevolume of tissue. For example, an image representation may depictacoustic attenuation, acoustic reflection, acoustic speed, and/or anysuitable property of the tissue.

In one embodiment, a slice may correspond to regions of a volume oftissue scanned in an anterior to posterior manner (e.g., in coronalslices); however, the slices may correspond to slices in any otherdirection (e.g., at any angle relative to the anterior-posteriordirection, in an inferior-superior direction, at any angle relative tothe inferior-superior direction, in a medial-lateral direction, at anyangle relative to the medial-lateral direction, etc.). Each acousticdata point within an acoustic slice may be associated with a particularvalue on a grid, including a position along the sliced axis, such thatslices and the data may be ordered relative to each other to image thevolume of tissue.

FIG. 3 shows a schematic showing the generation of 3D renderings fromstacks of 2D images of three acoustic data types, in accordance withembodiments. FIG. 3 shows a processor, a display, an imaging tank 230,and a scanner table of FIG. 2A. A set of acoustic reflection data,I_(r), a set of acoustic speed data, I_(s), and a set of attenuationdata, I_(a), corresponding to stacks of slices of the volume of tissuemay each be collected. Additionally, FIG. 3 shows a 3D rendering of avolume of tissue corresponding to each data type. In another embodiment,generating a 3D acoustic rendering may comprise converting a 3D acousticdata set obtained by scanning in a 3D manner, directly to a 3Drendering.

The 3D renderings of any type of acoustic data may be combined or mergedin whole or in part. In one embodiment, a merged rendering may comprisecombining 3D renderings of at least two types of image data. In anotherembodiment, a merged rendering may comprise combining at least a portionof the set of 2D images from at least two types of image data. Anysuitable formula or algorithm may be used to merge or fuse the variousrenderings into a single rendering.

Acoustic data may be rendered, for example, using methods described inU.S. Pat. Nos. 8,663,113; 9,113,835; and U.S. patent application Ser.No. 13/756,851; U.S. patent application Ser. No. 13/756,864; U.S. patentapplication Ser. No. 14/015,459; U.S. patent application Ser. No.14/703,746; U.S. patent application Ser. No. 14/817,470; U.S. patentapplication Ser. No. 14/819,091; and P.C.T. International Pat. App. Pub.No. WO2017040866, which are each incorporated herein in their entiretyby reference. At a step 130 of the method 100, a first reflectionrendering that characterizes sound reflection may be generated from theplurality of acoustic signals, the first reflection rendering comprisinga first distribution of reflection values across a region of the volumeof tissue. A step 130 may be performed using a processor 210 includedwith or coupled to an ultrasound tomography scanner 200 of steps 110.Additionally or alternatively, step 130 may be performed on any suitableprocessor. Step 130 may generate a reflection rendering based on the setof acoustic signals from steps 110 and 120. Additionally oralternatively, a reflection rendering may be generated based on a set ofacoustic signals from any suitable data. The reflection rendering mayutilize envelope detected reflection data (ERF), raw radiofrequencyreflection signals (e.g., REF image data, “radiofrequency”, or RF data),which can be converted to a flash B-mode ultrasound image, and/or anysuitable ultrasound image. The distribution of acoustic reflectionsignals may characterize a relationship (e.g., a sum, a difference, aratio, etc.) between the reflected intensity and the emitted intensityof an acoustic waveform, a change in the acoustic impedance of a volumeof tissue, and/or any other suitable acoustic reflection parameter.

A stack of 2D acoustic reflection images may be derived from changes inacoustic impedance of the tissue and may provide echo-texture data andanatomical detail for the tissue. Generating an acoustic reflectionrendering may additionally and/or alternatively comprise generating athree-dimensional (3D) acoustic reflection rendering that may be avolumetric representation of the acoustic reflectivity of the volume oftissue. The reflection rendering can characterize a volume of tissuewith a distribution of one or more of: fat tissue (e.g., fattyparenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymaltissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue,fibroadenomas, etc.), and any other suitable tissue type within thevolume of tissue.

In some embodiments, the acoustic reflection rendering may be generatedfrom a distribution of acoustic reflection signals received from anarray of transducer elements transmitting and receiving at a frequencygreater than the frequency of the array of transducer elements used togenerate a rendering from another acoustic data type including, forexample, the sound speed rendering or the attenuation rendering. Inother embodiments, the acoustic reflection rendering may be generatedfrom a distribution of acoustic reflection signals received from anarray of transducer elements transmitting and receiving at a frequencyless than the frequency of the array of transducer elements used togenerate a rendering from another acoustic data type including, forexample, the sound speed rendering or the attenuation rendering. The lowfrequencies (˜1 MHz) may provide information on specular reflections(down to ˜1 mm); however, imaging at higher frequencies (˜1 to 5 MHz)may be better able to image the sub-mm granularity that providesinformation on speckle patterns. Therefore, it may be beneficial togenerate a particular acoustic rendering at a particular frequency.

In some embodiments, the pulse echo contribution to the first reflectionimage of step 130 of a method 100 may be generated by the followingmethod, as summarized below:

-   -   a. Specify path and read in REF image.    -   b. Remove all negative values from the REF image, such that        R_(f)=REF|_(>0)    -   c. Perform variance normalization of the RF images.    -   d. Mitigate effect of outliers on pixel intensity distribution.    -   e. Smooth image.    -   f. Calculate the logarithm.    -   g. Remove outliers.    -   h. Normalize image values.    -   i. Remove ring feature (edge of transducer).

The method may comprise calculation of a number of mathematicalrelations including but not limited to, for example, averaging,truncation, normalization, smoothing, calculating the logarithm,addition, subtraction, multiplication, division, and any other simplemathematical relation that is known to one of ordinary skill in the art.The method may comprise smoothing the image data. The image may besmoothed by one or more algorithms such as but not limited to, forexample, convolution with another function (such as a Gaussian or aLorentzian), adjacent averaging, Fourier filtering, and any otheralgorithm suitable to smooth an image of a volume of tissue.

While a method for processing the first reflection image is shown above,in accordance with some embodiments, a person of ordinary skill in theart will recognize many adaptations and variations. Further, one or moresteps shown above may be deleted or repeated, additional steps can beadded, and the steps can be performed in any order.

At a step 140 of the method 100, a sound speed rendering thatcharacterizes sound speed may be generated from the plurality ofacoustic signals. The sound speed rendering may comprise a distributionof sound speed values across the region of the volume of tissue. A step140 may be performed using a processor 210 included with or coupled toan ultrasound tomography scanner 200 of step 110. Additionally oralternatively, step 140 may be performed on any suitable processor. Step140 may generate a sound speed rendering based on the set of acousticsignals from steps 110 and 120. Additionally or alternatively, a soundspeed rendering may be generated based on a set of acoustic signals fromany suitable data. The sound speed map may be generated based on aprocessing of sound transmission signals that pass through the volume oftissue in addition to backscattered signals from the volume of tissue.The sound speed map may characterize a part of the complex valuedultrasound impedance of the volume of tissue, the rate of travel of awaveform through the volume of tissue, a ratio of distance of travelthrough the volume of tissue over time between transmission anddetection, or any other suitable acoustic speed parameter.

A stack of 2D acoustic sound speed images may be derived from thecomplex-valued impedance of the tissue and may provide anatomical detailof the tissue, wherein the impedance comprises a real portioncorresponding to a phase velocity, and an imaginary portioncorresponding to a sound attenuation. Generating an acoustic sound speedrendering may additionally and/or alternatively comprise generating athree-dimensional (3D) acoustic sound speed rendering that may be avolumetric representation of the acoustic sound speed of the volume oftissue. The sound speed rendering can characterize a volume of tissuewith a distribution of one or more of: fat tissue (e.g., fattyparenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymaltissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue,fibroadenomas, etc.), and any other suitable tissue type within thevolume of tissue.

Additionally or alternatively, the sound speed rendering may begenerated from a waveform sound speed method. Such a method may comprisegenerating an initial sound speed rendering based on a time traveltomography algorithm. Additionally or alternatively, the initial soundspeed rendering may be iteratively optimized until ray artifacts may bereduced to a pre-determined threshold for each of a plurality of soundfrequency components. Such a method may comprise the method described inU.S. application Ser. No. 14/817,470, which is incorporated herein inits entirety by reference.

At a step 150 of the method 100, a second reflection rendering thatcharacterizes sound reflection may be generated from the sound speedrendering, the second reflection rendering comprising a seconddistribution of reflection values across the region of the volume oftissue. A step 150 may be performed using a processor 210 included withor coupled to an ultrasound tomography scanner 200 of steps 110.Additionally or alternatively, step 150 may be performed on any suitableprocessor. Step 150 may generate a sound speed rendering based on theset of acoustic signals from steps 110 and 120. At a step 150, a secondsound reflection rendering may be calculated from a sound speedrendering using a relation that involves a gradient with respect toposition of the sound speed pixel or voxel. The distribution of acousticreflection signals may characterize a relationship (e.g., a sum, adifference, a ratio, etc.) between the reflected intensity and theemitted intensity of an acoustic waveform, a change in the acousticimpedance of a volume of tissue, and/or any other suitable acousticreflection parameter.

A stack of 2D acoustic reflection images may be derived from changes inacoustic impedance of the tissue and may provide echo-texture data andanatomical detail for the tissue. Generating an acoustic reflectionrendering may additionally and/or alternatively comprise generating athree-dimensional (3D) acoustic reflection rendering that may be avolumetric representation of the acoustic reflectivity of the volume oftissue. The reflection rendering can characterize a volume of tissuewith a distribution of one or more of: fat tissue (e.g., fattyparenchyma, parenchymal fat, subcutaneous fat, etc.), parenchymaltissue, cancerous tissue, abnormal tissue (e.g., fibrocystic tissue,fibroadenomas, etc.), and any other suitable tissue type within thevolume of tissue.

The acoustic impedance (z) may be defined as z=ρc where, ρ is thedensity and c is the speed of sound. Reflections occur when there arespatial gradients (e.g., changes) in the acoustic impedance. Such agradient can be mathematically defined as the rate of change of theacoustic impedance, z, with respect to position r=r(x,y):

$\frac{\partial z}{\partial r} = {{\frac{\partial\rho}{\partial r}c} + {\frac{\partial c}{\partial r}\rho}}$

Variations in density as well as speed of sound contribute to thegradient; however, waveform algorithms do not solve for densityindependently of speed of sound. The waveform algorithms make thesimplifying assumption that ρ=constant. Consistent with this assumption,the waveform contribution to reflection imaging may be defined as:

$R_{w} = {\frac{\partial z}{\partial r} = {{\frac{\partial c}{\partial r}\rho} = {{constant} \cdot \frac{\partial c}{\partial r}}}}$

Thus, R_(w) is assumed to be linearly proportional to the gradient ofthe sound speed image. This contribution provides a margin definitionand texture contrast. Alternatively, the waveform algorithm may solvefor both ρ and c so that the full form of the gradient can becalculated.

In some embodiments, the acoustic reflection rendering may be generatedfrom a distribution of acoustic sound speed signals received from anarray of transducer elements transmitting and receiving at a frequencygreater than the frequency of the array of transducer elements used togenerate a rendering from another acoustic data type including, forexample, the first reflection speed rendering or an attenuationrendering. In other embodiments, the acoustic reflection rendering maybe generated from a distribution of acoustic reflection signals receivedfrom an array of transducer elements transmitting and receiving at afrequency less than the frequency of the array of transducer elementsused to generate a rendering from another acoustic data type including,for example, the sound speed rendering or the attenuation rendering. Thelow frequencies (˜1 MHz) may provide information on specular reflections(down to ˜1 mm); however, imaging at higher frequencies (˜1 to 5 MHz)may be better able to image the sub-mm granularity that providesinformation on speckle patterns. Therefore, it may be beneficial togenerate a particular acoustic rendering at a particular frequency.

In some embodiments, the second reflection image may be generated by thefollowing method, as summarized below:

-   -   a. Read in waveform sound speed image and make it the same size        as the first reflection image    -   b. Define a region that does not include the ring and calculate        a gradient, R_(w)=|∇c|    -   c. Eliminate outliers.    -   d. Smooth image.    -   e. Calculate the logarithm.    -   f. Normalize the image values.

The method provides the calculation of a gradient. The gradient maycomprise one or more algorithms such as image processing algorithms thatmay be used to “find edges” in an image. Such edge finding algorithmsmay include but are not limited to Sobel-Feldman operator, the Scharroperator, the Prewitt operator, and the Roberts Cross operator, and anyother suitable method for finding edges in an image. In someembodiments, the gradient may be calculated using a Sobel-Feldman edgedetector to highlight sharp changes in intensity in the selected image.A plurality of convolution kernels may be used to generate vertical andhorizontal derivatives. The final image may be produced by combining thehorizontal and vertical derivatives.

The method provides calculation of a number of mathematical relationsincluding but not limited to, for example, averaging, truncation,normalization, smoothing, calculating the logarithm, addition,subtraction, multiplication, division, and any other simple mathematicalrelation that is known to one of ordinary skill in the art. The methodprovides smoothing the image data. The image may be smoothed by one ormore algorithms such as but not limited to, for example, convolutionwith another function (such as a Gaussian or a Lorentzian), adjacentaveraging, Fourier filtering, and any other algorithm suitable to smoothan image of a volume of tissue.

While a method for processing the waveform sound speed image is shownabove, in accordance with some embodiments, a person of ordinary skillin the art will recognize many adaptations and variations. Further, oneor more steps shown above may be deleted or repeated, additional stepscan be added, and the steps can be performed in any order.

The reflection renderings of step 130 and step 150 of the method 100 mayeach be generated from acoustic data received from transducers whichemit, detect, and/or may be sensitive to different frequencies. Thefirst reflection rendering of step 130 may be generated from acousticdata received from a higher frequency (e.g., radiofrequency) sensitivetransducer. A higher frequency transducer may increase the resolution ofthe acoustic data, allowing for smaller (e.g., less than 1 mm) featuresto be resolved; however, the transmission of higher frequency sound maybe decrease with increased frequency. The second reflection rendering ofstep 150 may be generated from acoustic data received from a lowerfrequency transducer. A lower frequency may allow for greater ultrasoundpenetration, increasing the contrast between lower sound speed andhigher sound speed regions of the volume of tissue. The secondreflection rendering from the lower frequency transducer may also showgreater contrast between regions of high and low reflectivity.

At a step 160 of the method 100, one or more combined images may berendered based on the first reflection rendering and the secondreflection rendering, thereby generating the enhanced image of thevolume of tissue. The two images may be combined by a relation thatincludes: an element wise average or a weighted average, an element wisesum or a weighted sum, an element wise product or a weighted product, aconvolution of the two images, or any other mathematical relationshipsufficient to render a combined image. This relation may additionallyinclude other steps including, for example, averaging, truncation,renormalization, smoothing (such as by convolution with anotherfunction, adjacent averaging, Fourier filtering, etc.), addition,subtraction, multiplication, division, or any other appropriatemathematical relation appropriate to render a combined image.

In some embodiments, the first and the second reflection renderings,R_(f) and R_(w), may be combined by the following method, as summarizedbelow:

-   -   a. Reduce weight of R_(w)    -   b. Add the two components

While a method for combining two reflection images is shown above, inaccordance with some embodiments, a person of ordinary skill in the artwill recognize many adaptations and variations. Further, one or moresteps shown above may be deleted or repeated, additional steps can beadded, and the steps can be performed in any order.

FIG. 4 shows exemplary coronal slices of several three dimensionalrenderings of each of several acoustic data types of the same patientbreast comprising a cancerous lesion at various steps in a method 100 ofgenerating an enhanced image, in accordance with embodiments. Slice 430shows an exemplary RF reflection image, and slice 432 shows a magnifiedimage of a lesion in a volume of tissue in slice 430. Step 130 of amethod 100 may comprise generation of such a RF reflection image as 430or 432. Slice 440 shows an exemplary waveform sound speed imagegenerated from the method described the reference incorporated above.Step 140 of a method 100 may comprise generation of such a waveformsound speed image as 440. Slice 450 shows an exemplary second reflectionrendering, and slice 452 shows a magnified image of a lesion in a volumeof tissue in slice 450. Step 150 of a method 100 may comprise generationof such a second reflection rendering as 450 or 452. Slice 460 shows anexemplary enhanced reflection image, and slice 462 shows a magnifiedimage of a lesion in a volume of tissue in slice 460. Step 160 of amethod 100 may comprise generation of such an enhanced reflection imageas 460 or 462.

Magnified images 432, 452, and 462 may correspond to images at steps130, 150, and 160 of the method 100, respectively. Images 432, 452 and462 show increasing improvement in contrast, resolution, and conspicuityof spiculations around a lesion.

For example, an enhancement in resolution may comprise an increase inresolution of the image from an initial resolution of greater than about1 mm, greater than about 2 mm, greater than about 3 mm, greater thanabout 4 mm, greater than about 5 mm, greater than about 6 mm, greaterthan about 7 mm, greater than about 8 mm, greater than about 9 mm, orgreater than about 10 mm; to an enhanced resolution of less than about1.0 mm, less than about 0.9 mm, less than about 0.8 mm, less than about0.7 mm, less than about 0.6 mm, less than about 0.5 mm, less than about0.4 mm, less than about 0.3 mm, less than about 0.2 mm, or less thanabout 0.1 mm. For example, an enhancement in contrast may comprise anincrease in contrast of the image by a factor of at least about 2, atleast about 3, at least about 4, at least about 5, at least about 6, atleast about 7, at least about 8, at least about 9, or at least about 10.For example, an enhancement may comprise an increase in conspicuity oflesions in the volume of tissue by a factor of at least about 2, atleast about 3, at least about 4, at least about 5, at least about 6, atleast about 7, at least about 8, at least about 9, or at least about 10.For example, an enhancement may comprise an increase in specificity oflesion characterization in the volume of tissue such that thespecificity is at least about 75%, at least about 80%, at least about85%, at least about 90%, or at least about 95%. In some embodiments, thetissue comprises breast tissue.

Since cancerous lesions may show more frequent spiculations, thecombined image 462 may improve the classification of cancerous massesfrom fibroadenomas, or other benign masses. The combined reflectionimage may improve the conspicuity of the boundary around a lesion. Forexample, an enhancement may comprise an increase in conspicuity oflesions in the volume of tissue by a factor of at least about 2, atleast about 3, at least about 4, at least about 5, at least about 6, atleast about 7, at least about 8, at least about 9, or at least about 10.Since cancerous lesions may show irregular boundaries, the combinedimage may improve the classification of cancerous masses fromfibroadenomas, which may have smoother and more regular boundaries. Asshown in FIG. 4, as the method progresses, the reflection images showincreased contrast and resolution. Additionally, the edge of thecancerous lesion and the spiculations in the volume of breast in FIG. 4become significantly more conspicuous.

In other embodiments, the method of generating an enhanced image of avolume of tissue may additionally comprise correction of the enhancedreflection image for contributions from fat tissue. Some frequenciesused to create reflection images of fatty tissues can be relativelyanechoic. This can be less than ideal for the reviewing radiologistbecause the fatty tissues may compete with lesions for conspicuity. Insome embodiments, the method provides a method to artificially boostregions of fat in brightness to levels comparable to the otherhyperechoic structures in the breast. In some embodiments the methodrelies upon independent knowledge of the distribution of fat in thebreast obtained from the sound speed images. Using numericalthresholding fat may be differentiated from other tissue components ofthe breast by its low sound speed. The thresholding method may renderthe fatty tissue approximately the same echogenicity as the densertissues, such that only lesions remain hypoechoic in the image stack.

FIG. 5 shows exemplary coronal slices the same patient breast from FIG.4 additionally comprising an enhanced image after fat correction, inaccordance with embodiments. Slice 430 shows an exemplary RF reflectionimage, and slice 432 shows a magnified image of a lesion in a volume oftissue in slice 430. Slice 460 shows an exemplary enhanced reflectionimage, and slice 462 shows a magnified image of a lesion in a volume oftissue in slice 460. Slice 570 shows an exemplary enhanced reflectionimage after fat correction, and slice 572 shows a magnified image of alesion in a volume of tissue in slice 570.

In some embodiments, the correction of the enhanced reflection image forcontributions from fat may be implemented by the following method, assummarized below:

-   -   a. Read in waveform sound speed image    -   b. Define a threshold for fat sound speed and a threshold for        dense tissue    -   c. Normalize relative to the threshold for fat sound speed    -   d. Define a transformation that yields a greater coefficient for        c values below the threshold and unity for c values above the        threshold    -   e. Read in the enhanced reflection image    -   f. Apply the transformation to the enhanced reflection image

In some embodiments, the mathematical operation that defines thetransformation may have the general form of a Gaussian function, such asone given by:

${R_{corr} = {R_{wafer} \cdot \left\lbrack {1 + e^{- \frac{{({c - c_{f}})}^{2}}{\sigma^{2}}}} \right\rbrack}},$

where c=speed of sound from the sound speed image and c_(f) is the speedof sound of fat. Thus, each pixel in the R_(wafer) image may be modifiedby a value that depends on how far away the corresponding c may be fromthat of fat. Thus, for values of c=c_(f), R_(wafer) may be multiplied by2, while for distant values of c (c>>c_(f)), the multiplying factor maydecline to 1 (e.g., no modification to pixel value). The scalingconstant σ may govern the contrast of the fat relative to denser tissuesin the final R_(corr) image.

In other embodiments, the mathematical operation that defines thetransformation may have the form of a step function. In otherembodiments, the mathematical operation may comprise any type functionthat may represent a probability distribution of a single randomvariable such as but not limited to, for example, a Lorentziandistribution; a Voigt distribution; a logistic distribution; a Laplacedistribution; a Landau distribution; a Student's t distribution; anerror function; a Dirac delta function; and a Weibull distribution.

While a method for processing the waveform sound speed image is shownabove, in accordance with some embodiments, a person of ordinary skillin the art will recognize many adaptations and variations. Further, oneor more steps shown above may be deleted or repeated, additional stepscan be added, and the steps can be performed in any order.

In other embodiments, the method of generating an enhanced image of avolume of tissue may additionally comprise classifying, based on the oneor more combined images, different types of lesions in the volume oftissue as at least one of a cancerous tumor, a fibroadenoma, a cyst, anonspecific benign mass, and an unidentifiable mass. The method ofclassifying may comprise the method of characterizing a volume of breasttissue based on plurality of prognostic parameters interior and exteriorto a region of interest described herein, which method may improve thespecificity of lesion characterization by up to 10%, for example, withina range defined between any two of the following values: about 0.1%,about 1%, about 5%, and about 10%. Additionally or alternatively,classifying a lesion may be performed based on qualitative observationsby skilled user, according to an existing classification method, such asBreast Imaging Reporting and Data System (BI-RADS). Additionally oralternatively, classifying a lesion may be performed based on anyaccepted classification method using a reflection image.

FIG. 6A shows exemplary coronal slices of the same patient breastcomprising a cancerous lesion and a malignant node from different typesof acoustic renderings, in accordance with embodiments. FIG. 6A showshow the method of generating an enhanced reflection image describedherein may aid in classification of a lesion. Slice 610 is a “Bent-Ray”sound speed image; slice 620 is a “waveform” sound speed image; slice630 is a “B-mode” reflection image; and slice 640 is an enhancedreflection image based on the methods disclosed herein. FIG. 6B showsmagnified images 622, 632, and 642 of the acoustic rendering in 620, 620and 640, respectively, in accordance with embodiments. Images 610 and620 highlight the improvement in resolution of waveform based soundspeed images, such as image 620, over time of flight sound speed images,such as image 610. In the magnified images, the enhanced reflectionimage 642 shows more conspicuous edges around the cancer (CA) and themalignant node, more conspicuous spiculations around the cancer, andincreased image contrast over the “B mode” reflection image.

Margin Boundary Selection

Embodiments of the present disclosure provide a method forcharacterizing a volume of breast tissue of a patient. The method may beimplemented by a computer comprising one or more processors and computerreadable media comprising instructions. FIG. 7 shows an exemplary method700 for characterizing a volume of breast tissue of a patient, inaccordance with some embodiments. A method 700 may comprise emittingtoward the volume of tissue a plurality of acoustic waveforms anddetecting from the volume of tissue a plurality of acoustic signals witha transducer, wherein the transducer may comprise an array of ultrasoundtransmitters and an array of ultrasound receivers configured to surroundthe volume of tissue 710. The method 700 may further comprise receivingfrom a transducer, a plurality of acoustic signals derived from acousticwaveforms transmitted through the volume of tissue 720. The method 700may further comprise generating from the plurality of acoustic signals,a three-dimensional acoustic rendering that characterizes soundpropagation within the volume of tissue 730. The method 700 may furthercomprise receiving input from a user corresponding to a user-selectedregion of interest 740. The method 700 may further comprise generatingfrom the acoustic rendering a first plurality of prognostic parameterscorresponding to sound propagation interior to the user-selected regionof interest 750. The method 700 may further comprise generating from theacoustic rendering a second plurality of prognostic parameterscorresponding to sound propagation exterior to the user-selected regionof interest 760. The method 700 may further comprise characterizing alesion within the volume of tissue using the first plurality of interiorprognostic parameters and the second plurality of exterior prognosticparameters 770.

In some embodiments, method 700 may function to characterize a volume oftissue (e.g., a whole breast, another organ) according to a rendering ofultrasound images that enhance target objects within a field of view.Additionally or alternatively, the volume of tissue may comprise adistribution of one or more of: fat tissue (e.g., fatty parenchyma,parenchymal fat, subcutaneous fat, etc.), parenchymal tissue, canceroustissue, abnormal tissue (e.g., fibrocystic tissue, fibroadenomas, etc.),and any other suitable tissue type within the volume of tissue. Method700 may be used to characterize tissue of a human breast, but mayadditionally or alternatively be used to characterize tissue of an arm,leg, other appendage, and/or any suitable volume of tissue in a human orother animal. In relation to current ultrasound methods and systems,method 700 may improve specificity in characterization of types ofmasses by up to 10%, for example, within a range defined between any twoof the following values: about 0.1%, about 1%, about 5%, and about 10%.Such masses may include but are not limited to: a cancerous tumor, afibroadenoma, a cyst, a nonspecific benign mass, and an unidentifiablemass. Method 700 may, however, function to enable diagnosis, monitoring,and/or characterization of a volume of tissue in any other suitablemanner.

In some embodiments, method 700 may be used to characterize the tissueto facilitate diagnoses of cancer, assess its type and determine itsextent (e.g., to determine whether a mass in the tissue may besurgically removable), or to assess risk of cancer development (e.g.,measuring breast tissue density). In yet another embodiment, method 700may be used to characterize and/or investigate particular aspects of thetissue, such as to determine whether a mass in the tissue may be atumor, cyst, fibroadenoma, or other kind of mass. Method 700 may be usedin any suitable application for imaging a volume of tissue or othersuitable object. Method 700 may be implemented, at least in part, by wayof an embodiment, variation, and/or example of the system 200 describedin the section titled “Ultrasound Tomography System” below; however,method 700 may additionally or alternatively be implemented using anyother suitable system.

While FIG. 7 shows a method of generating an enhanced image of a volumeof tissue, in accordance with embodiments, a person of ordinary skill inthe art will recognize many adaptations and variations. Further, one ormore steps shown in FIG. 7 may be deleted or repeated, additional stepscan be added, and the steps can be performed in any order.

At a step 710 of the method 700, a plurality of acoustic waveforms maybe emitted toward the volume of tissue and a plurality of acousticsignals may be detected from the volume of tissue with a transducer. Thetransducer may comprise an array of ultrasound transmitters and an arrayof ultrasound receivers configured to surround the volume of tissue. Ata step 720 of the method 700, a plurality of acoustic signals derivedfrom acoustic waveforms transmitted through the volume of tissue may bereceived by a computer from a transducer. Steps 710 and 720 function togather acoustic data from which renderings of the volume of tissue maybe derived in other steps of the method 700. At step 710 of the method700, emitting acoustic waveforms and detecting a set of acoustic signalsmay be performed with an ultrasound tomographic scanner 200, for exampleas described in the section titled “Ultrasound Tomography System”, andusing methods similar to those described in the incorporated references.However, any suitable ultrasound device or scanner may be used.Additionally or alternatively, a step 710 and a step 720 may beperformed using the methods described in the section titled “Emittingand Receiving Acoustic signals”.

At a step 730 of the method 700, a three-dimensional acoustic renderingthat characterizes sound propagation within the volume of tissue may begenerated from the plurality of acoustic signals. The acoustic signalsmay be processed by a computer as acoustic data. Acoustic data may beused to generate one or more “stacks” of 2D images corresponding to aseries of “slices” of the volume of tissue for each measuredacoustomechanical parameter. Each stack of 2D image data may compriseacoustic data associated with a particular parameter or property of thetissue, for example, any type of acoustic data such as acousticreflection, acoustic sound speed, and acoustic attenuation. Theprocessor 210 may additionally or alternatively generate athree-dimensional volumetric rendering based on the stack oftwo-dimensional images, and/or generate a three-dimensional volumetricrendering directly based on the received acoustic data. An imagerepresentation of any portion of the volume of tissue may depict any oneor more acoustomechanical properties of the volume of tissue. Forexample, an image representation may depict acoustic attenuation,acoustic reflection, acoustic speed, and/or any suitable property of thetissue.

Additionally or alternatively, a step 730 may be performed using amethod 100 for generating an enhanced image of a volume of tissuedescribed herein. Additionally or alternatively, a step 730 may beperformed using the methods described in the section titled “Processingof Acoustic Data” and in the methods in the references incorporatedtherein. Such methods may include generating a waveform sound speedrendering and generating a first reflection rendering.

In one embodiment, a slice may correspond to regions of a volume oftissue scanned in an anterior to posterior manner (e.g., in coronalslices); however, the slices may correspond to slices in any otherdirection (e.g., at any angle relative to the anterior-posteriordirection, in an inferior-superior direction, at any angle relative tothe inferior-superior direction, in a medial-lateral direction, at anyangle relative to the medial-lateral direction, etc.). Each acousticdata point within an acoustic slice may be associated with a particularvalue on a grid, including a position along the sliced axis, such thatslices and the data may be ordered relative to each other to image thevolume of tissue.

Additionally or alternatively, generating a set of 2D acousticattenuation images may be derived from the imaginary part of theacoustic impedance of the tissue and may yield anatomical detail for thetissue. Generating an acoustic attenuation rendering may additionallyand/or alternatively comprise generating a three-dimensional (3D)acoustic attenuation rendering that may be a volumetric representationof the acoustic attenuation of the volume of tissue. The attenuationrendering can characterize a volume of tissue with a distribution of oneor more of: fat tissue (e.g., fatty parenchyma, parenchymal fat,subcutaneous fat, etc.), parenchymal tissue, cancerous tissue, abnormaltissue (e.g., fibrocystic tissue, fibroadenomas, etc.), and any othersuitable tissue type within the volume of tissue. Additionally oralternatively, generating an acoustic attenuation rendering may comprisea method described in the references incorporated herein.

The 3D renderings of any type of acoustic data may be combined or mergedin whole or in part. In one embodiment, a merged rendering may comprisecombining 3D renderings of at least two types of image data. In anotherembodiment, a merged rendering may comprise combining at least a portionof the set of 2D images from at least two types of image data. Anysuitable formula or algorithm may be used to merge or fuse the variousrenderings into a single rendering.

At a step 740 of the method 700, input may be received from a usercorresponding to a user-selected region of interest. A region ofinterest (ROI) may be identified by a user based on the 3D renderings ofany form acoustic data including acoustic attenuation, acoustic soundspeed, and acoustic reflection and additionally including combined ormerged renderings. The ROI may correspond to a mass within for example abreast tissue. The mass may be for example, a cancerous mass, a benignfibroadenoma, a cyst, another benign finding, an unidentifiable mass(for example, there may be no finding), or any suitable characterizationor classification. In one embodiment, the ROI may be selected by a user,for example, by tracing the ROI “free-hand” or drawing a simple shapesuch as a circle or ellipse.

Additionally or alternatively, the selection of an ROI may be aided oroptimized by a computer-implemented algorithm, wherein the computercomprises a processor with instructions to implement the algorithm. Theprocessor may aid or optimize selection of an ROI based on thresholdvalues of any acoustic data type and/or multiples of data typesincluding combinations of data types. The processor may aid or optimizeselection of an ROI based on a known morphology, such as through the useof image recognition algorithms.

The ROI may comprise a margin boundary between the ROI and thesurrounding tissue. FIG. 8A shows exemplary ROIs comprising varioususer-selected and/or computer selected margin boundaries andadditionally comprising varying boundary morphologies, in accordancewith embodiments. The margin boundary may be completely identified atthe start of analysis; however, additionally or alternatively, themargin boundary may be incomplete at the start of analysis and optimizedby a computer program. In FIG. 8A, the left three margin boundaries showvarious degrees of completeness at the start of analysis. The marginboundary 810 is greater than two-thirds complete; margin boundary 820 isbetween one-third and two-thirds complete; margin boundary 830 is lessthan ⅓ complete.

The margin boundary may also comprise a morphology which may be used ina classifier model. Such morphologies may comprise, for example, thosewith smooth edges, those with irregular and/or rough edges, for example,those which have one or a plurality of elongate elements or those whichmay be speculated, those which may be predominantly round or ellipsoid,or any other shape which a lesion in a tissue may reasonably take.Margin boundaries 840 and 850 are complete and show exemplarymorphologies of the margin boundary. Margin boundary 840 shows anirregular margin boundary. Margin boundary 850 shows significantspeculation, such as may indicate a cancerous lesion.

From a selected ROI with an identified margin boundary, one or aplurality of interior and exterior regions of interest may beidentified. The interior region(s) of interest may comprise one or aplurality of layers starting at the margin boundary and continuingfurther inside the ROI. The exterior regions of interest may compriseone or a plurality of layers starting at the margin boundary andcontinuing further outside the ROI. The interior and exterior layers ofthe ROI may each comprise layers classified as “near” and “distant”. The“near” layers may be classified as being close to the boundary. The“distant” layers may be classified as being far from the boundary. Forexample, the interior layers of the region of interest may compriselayers near the boundary and distant from the boundary. For example, theexternal layers of the region of interest may comprise layers near theboundary and distant from the boundary.

FIG. 8B shows an exemplary ellipsoidal ROI 860 with a margin boundaryand internal and external layers, in accordance with embodiments. Themargin boundary may be selected by a user and additionally oralternatively selected by a user and optimized with the aid of acomputer implemented algorithm. The exemplary ROI in FIG. 8B isellipsoidal; however, an ROI may have any morphology that a lesion maytake, some of which are listed with reference to FIG. 8A. Additionallyor alternatively, the ROI in FIG. 8B may correspond to an initial ROIselected by a user to be optimized or assisted by a computer program.ROI 860 has an interior in grey and an exterior 870 in white. Theexterior of the ROI may extend further than the lines drawn around theROI in FIG. 8B.

In some embodiments, the interior of ROI 860 may be segmented intolayers shown with solid lines drawn inside the grey area. FIG. 8B showsan interior of a region of interest which has been segmented into 10layers; however, the interior of the ROI can be segmented into anynumber of layers. The layers may be evenly spaced or may get smaller orlarger from interior to exterior. Additionally or alternatively, theexterior of ROI 860 may be segmented into layers shown with solid linesdrawn outside of ROI 860. FIG. 8B shows an exterior of a region ofinterest which has been segmented into 10 layers; however, the exteriorof the ROI can be segmented into any number of layers. The layers may beevenly spaced or may get smaller or larger from interior to exterior.The ROI can be segmented into layers before or after finalization of anROI.

Additionally or alternatively, a set of layers interior or exterior tothe region of interest may be classified as “near” or “distant”.Exterior region 870 in FIG. 8B has five layers classified as “near” 872and five layers classified as “distant” 874. The number of layersclassified as near or distant may comprise any subset of layers interioror exterior to the region of interest. Additionally or alternatively,the layers may be divided evenly into near and distant or unevenly.Additionally or alternatively, the layers classified near and distantmay overlap such that an individual layer may fall into both the nearand distant classification. The layers may be classified as near ordistant before or after finalization of the ROI.

Prognostic Parameters

The selected ROI may be characterized using a classifier model based ona set of prognostic parameters. Set of prognostic parameters maycomprise one or many types of acoustic data corresponding to soundpropagation in a volume of tissue. Such types of acoustic data includesbut is not limited to, for example: quantitative acoustic data (e.g.,acoustic sound speed, acoustic attenuation, and acoustic reflection),quantitative morphological data (e.g., an area, diameter, ellipticity,etc. of an ROI), and qualitative morphological data (e.g., a userassessed parameter). Additionally or alternatively, the classifier modelmay use threshold values of any prognostic parameter and/or multiples ofprognostic parameters. A threshold value of a prognostic parameter maybe selected from a known value inherent to a lesion type. A thresholdvalue of a prognostic parameter may be selected by a user. A thresholdvalue of a prognostic parameter may be selected based on acomputer-implemented algorithm. A selected threshold value and/orcombination of threshold values may be optimized by such an algorithm inorder to improve characterization of a lesion.

At a step 750 of the method 700, a first plurality of prognosticparameters corresponding to sound propagation interior to theuser-selected region of interest are generating from the acousticrendering. At a step 760 of the method 700, a second plurality ofprognostic parameters corresponding to sound propagation exterior to theuser-selected region of interest may be generated from the acousticrendering. Each layer, subset of layers, classification of layers,and/or ROI may have one or many associated quantitative prognosticparameters. Quantitative prognostic parameters may comprise, forexample, a mean, a median, a mode, a standard deviation, andvolume-averages thereof of any acoustic data type. A quantitativeprognostic parameter may be calculated from a combination of data types.For example, a quantitative prognostic parameter may comprise adifference of prognostic parameters between a region in the interior ofthe ROI and in the exterior of the ROI. In another example, aquantitative prognostic parameter may comprise a difference betweenregions of interest, layers, classification of layers, etc. Aquantitative prognostic parameter may comprise a ratio of a prognosticparameter with, for example, another prognostic parameter, a knownbiological property, etc. Additionally or alternatively, a quantitativeprognostic parameter may be weighted by a spatial distribution.Additionally or alternatively, a quantitative prognostic parameter maybe calculated from a volume average of an acoustic data type over, forexample, a region of interest, a layer, a plurality of layers, aclassification of layers, etc.

Each layer, classification of layers, and/or ROI may have one or manyassociated qualitative prognostic parameters. One or more qualitativeprognostic parameters may be used in combination to generate otherqualitative prognostic parameters. Qualitative prognostic parameters maycomprise one or a combination of the shape, the sharpness, thearchitecture and/or other characteristics of the morphology renderings.The qualitative prognostic parameters may characterize any suitableaspect of the biomechanical property renderings. A qualitativeprognostic parameter may be converted by a user or a computer into asemi-quantitative prognostic parameter, such as “1” for an indistinctmargin and “2” for a sharp margin of the region of interest in theacoustic reflection rendering. As another example, a qualitativeprognostic parameter may be converted by a user or a computer to asemi-quantitative parameter such as a value on an integer scale (e.g., 1to 5) that classifies the degree to which the qualitative aspect isexpressed. For instance, margin sharpness of the region of interest inthe acoustic reflection rendering may be classified with a reflectionindex as “1” if it is very sharp, “3” if it is moderately indistinct, or“5” if it is very indistinct.

Qualitative, quantitative, and semi-quantitative prognostic parametersmay be combined in order to generate other extended prognosticparameters. These extended prognostic parameters may comprise theexisting Breast Imaging Reporting and Data System (BI-RADS), wherein alesion is characterized on an integer scale from 1 to 5, but may alsocomprise other extended prognostic parameters comprising acoustic data.The prognostic parameters disclosed herein may be time dependent. Thetime dependence of one or a plurality of prognostic parameters maycomprise a prognostic parameter. Although all of these quantitative andqualitative prognostic parameters may be determined, only a portion ofthese parameters may be determined.

Table 1 shows exemplary fields for organizing prognostic parameters byregion of interest and by classification of region of interest,including fields for the mean (e.g., the volume-average) and standarddeviation (e.g., the volume-standard-deviation) of a particular acousticdata types A, B, and C over a volume of tissue. The table also comprisesprognostic parameters associated with differences betweenclassifications of layers associated with a region of interest.

Region A B C Tumor (T) Mean Std. Dev. Peritumoral (P) Mean Std. Dev.Near peritumoral (nP) Mean Std. Dev. Distant peritumoral (dP) Mean Std.Dev. Relative Peri (T-P) Mean Std. Dev RelNearP (T-nP) Mean Std. Dev RelDistanP (T-dP) Mean Std. Dev

The region “tumor” characterizes a set of prognostic parametersassociated with acoustic data interior to a region of interest. Theregion “peritumoral” characterizes a set of prognostic parametersassociated with acoustic data exterior to a region of interest. Theregion “near peritumoral” characterizes a set of prognostic parametersassociated with a classification of layers which may be near to themargin boundary of the region of interest and may be exterior to theregion of interest. The region “distant peritumoral” characterizes a setof prognostic parameters associated with a classification of layerswhich may be distant to the margin boundary of the region of interestand may be exterior to the region of interest. The region “RelativePeri” characterizes a set of prognostic parameters associated with thedifference between a set of interior prognostic parameters less a set ofexterior prognostic parameters. The region “RelNearP” characterizes aset of prognostic parameters associated with the difference between aset of interior prognostic parameters less a set of prognosticparameters which may be associated with a classification of layers,which may be near to the margin boundary of the region of interest andmay be exterior to the region of interest. The region “RelDistanP”characterizes a set of prognostic parameters associated with thedifference between a set of interior prognostic parameters less a set ofprognostic parameters which may be associated with a classification oflayers, which may be distant to the margin boundary of the region ofinterest and may be exterior to the region of interest.

Table 1 also shows exemplary fields for organizing prognostic parametersby various acoustic data types. In some embodiments, individualprognostic parameters may correspond to a statistical metric, such asmean or a standard-deviation, over a volume, such as defined by aregion, a layer, or a classification of layers. Such data type mayinclude but are not limited to, for example, the margin boundary score,the mean enhanced reflection (ErfMean), the relative mean of theenhanced reflection interior and exterior to the ROI (ErfRelDist), thestandard deviation of the enhanced reflection (Erf_SD), the mean soundspeed (SSMean), the relative mean sound speed interior and exterior tothe ROI (SSRelDist), the standard deviation of the sound speed (SS_SD),the mean attenuation (AtMean), the standard deviation of the attenuation(At_SD), the mean of the attenuation corrected for the margin boundaryscore (Corr_At_Mean), and the standard deviation of the attenuationcorrected for the margin boundary score (Corr_At_SD).

Classifier Model

At a step 770 of the method 700, a lesion within the volume of tissuemay be characterized using the first plurality of interior prognosticparameters and the second plurality of exterior prognostic parameters.The prognostic parameters may be used within a classifier model in orderto classify, predict, or otherwise characterize the region of interest.The analysis may predict whether the region of interest may be acancerous mass, a benign fibroadenoma, a cyst, another benign finding,an unidentifiable mass (for example, there may be no finding), or anysuitable characterization or classification. However, the analysis mayadditionally and/or alternatively monitor trends of one or moreprognostic parameters over time, or for any suitable application. Thestep of analyzing the prognostic parameter may comprise the analysis ofmultiple prognostic parameters, which may be quantitative,semi-quantitative, qualitative, and/or extended.

FIG. 9A shows an exemplary classifier model 910 comprising a thresholdvalue of a single prognostic parameter, in accordance with embodiments.In some embodiments, prognostic parameter P₁ may be an extendedprognostic parameter comprising a user-selected classification of amargin boundary of a region of interest according to an integer scale.In some embodiments, classifier model 910 may be based entirely uponqualitative or quantitative prognostic parameters associated with soundpropagation in the volume of tissue. In other embodiments, classifiermodel 910 may be used in conjunction with another classifier model suchas 920 or 930, such that the output of one classifier model may be usedas a prognostic parameter in the input of another.

In some embodiments, the user-selected classification may be performedby a skilled operator, such as a medical professional, and in otherembodiments, the user-selected classification may be aided by a computerimplemented method or algorithm. In some embodiments, the classificationmay be performed in conjunction with observation of a waveform soundspeed rendering; however, the classification may be performed using animage or rendering of any acoustic data type, such as the acoustic soundspeed, acoustic attenuation, acoustic reflection, and an enhanced imagegenerated from the sound speed, the acoustic attenuation, and theacoustic reflection.

In some embodiments, the user-selected classification may be assessedfrom an existing classification method such as the BI-RADS criteria,which may be predominantly devoted to assessment of tumor shape, marginsand interaction with adjacent tissue. Such criteria as “shadowing” or“enhanced through transmission” in the BI-RADS may not be applicableultrasound tomographic methods described herein; however, other criteriamay be more sensitively detecting using ultrasound tomography, such asspecular reflections of benign mass capsules, or the spiculations and/orarchitectural distortions of many cancers. In other embodiments, anadapted user-selected classification system may be implemented, whichhas been optimized for ultrasound tomographic imaging. Such a method maybe based on a 5-point scale (the margin boundary score) that combinesUS-BI-RADS criteria for tumor margins as well as peritumoral tissueinteraction.

An exemplary use of classifier model 910 includes a threshold for theoperator assessed score at a value a, such that if P₁≥a the mass may bediagnosed as cancerous. For example, Example 1 includes clinical dataincluding diagnostic outcomes using the margin boundary score assessedfrom a waveform sound speed image for prognostic parameter P₁ and athreshold value of 3.

Another exemplary use of classifier model 910 includes a threshold forthe operator assessed score at a value a, such that if P₁>a the mass maybe diagnosed as cancerous. Another exemplary use of classifier model 910includes a threshold for the operator assessed score at a value a, suchthat if P₁≤a the mass may be diagnosed as cancerous. Another exemplaryuse of classifier model 910 includes a threshold for the operatorassessed score at a value a, such that if P₁<a the mass may be diagnosedas cancerous. Another exemplary use of classifier model 910 includesevaluating a function of the value a and diagnosing the mass ascancerous based on the evaluated function of the value a.

FIG. 9B shows an exemplary classifier model 920 comprising a thresholdvalue of an interior prognostic parameter and an exterior prognosticparameter, in accordance with embodiments. In some embodiments,classifier model 920 may be based entirely upon quantitative prognosticparameters associated with sound propagation in the volume of tissue. Inother embodiments, classifier model 920 may be based upon a combinationof quantitative, semi-quantitative, and/or extended prognosticparameters (e.g., a margin boundary score). In other embodiments,classifier model 920 may be used in conjunction with another classifiermodel such as 910 or 930 such that the output of one classifier modelmay be used as a prognostic parameter in the input of another.

An exemplary use of classifier model 920 includes a threshold for aninterior prognostic parameter P₂ at a value b and an exterior prognosticparameter P₃ at a value c, such that if P₂≥b and P₃≤c the mass may bediagnosed as cancerous. For example, Example 1 includes clinical dataincluding diagnostic outcomes using the volume-standard-deviation forthe acoustic attenuation for prognostic parameter P₂ with a thresholdvalue of 0.0347 and using the volume-average for the sound speedexterior to the region of interest (e.g., in the peritumoral region) forprognostic parameter P₃ with a threshold value of 1.51.

Another exemplary use of classifier model 920 includes a threshold foran interior prognostic parameter P₂ at a value b and an exteriorprognostic parameter P₃ at a value c, such that if P₂>b and P₃>c themass may be diagnosed as cancerous. Another exemplary use of classifiermodel 920 includes a threshold for an interior prognostic parameter P₂at a value b and an exterior prognostic parameter P₃ at a value c, suchthat if P₂≤b and P₃≤c the mass may be diagnosed as cancerous. Anotherexemplary use of classifier model 920 includes a threshold for aninterior prognostic parameter P₂ at a value b and an exterior prognosticparameter P₃ at a value c, such that if P₂<b and P₃<c the mass may bediagnosed as cancerous. Another exemplary use of classifier model 920includes evaluating a function or piece-wise function of two or morevariables (b, c, . . . ) and diagnosing the mass as cancerous based onthe evaluated function of the two or more variables. For example,example 1 includes clinical data for diagnosing based upon a function oftwo variables, the volume-standard-deviation of the attenuation and thevolume-standard-deviation of the sound speed in the tumoral region.

FIG. 9C shows an exemplary classifier model 930 comprising a mixedqualitative and quantitative metric for assessing a mass, in accordancewith embodiments. In some embodiments, classifier model 930 may be basedentirely upon quantitative prognostic parameters associated with soundpropagation in the volume of tissue. In other embodiments, classifiermodel 920 may be based upon a combination of quantitative,semi-quantitative, and/or extended prognostic parameters (e.g., a marginboundary score). In other embodiments, classifier model 930 may be usedin conjunction with another classifier model such as 910 or 920 suchthat the output of one classifier model may be used as a prognosticparameter in the input of another.

An exemplary use of classifier model 930 includes: an operator assessedscore P₁ with two threshold values at a and d; an interior prognosticparameter P₂ at a value e; and a relative prognostic parameter P₄calculated from a difference of an interior and an exterior prognosticparameter at a value f. If the operator assessed score P₁ is greaterthan or equal to a, the mass may be characterized as cancerous. If theoperator assessed score of the region of interest is greater than d andless than a, the relative prognostic parameter P₄ may be furtherincluded in the assessment. If the value of P₄ is greater than f thelesion may be characterized as cancerous. If the operator assessed scoreP₁ is less than or equal to d, interior prognostic parameter P₂ may befurther included in the model. If the value of P₂ is greater than orequal to e the lesion may be classified as cancerous.

In the above use of classifier 930, P₁ may be the margin boundary scorewith thresholds at 2 and 4. If the value of P₁ is 3, P₄ may be aprognostic parameter corresponding to the volume-averaged enhancedreflectance in the tumoral region less the volume-averaged enhancedreflectance in the distant peritumoral region. If the difference is ≥to−34.6, the mass may be assessed as cancerous. If the P₁ is 1 or 2, P₃may be the volume-standard-deviation of the corrected acousticattenuation in the region of interest. If the standard deviation is≥0.15 the cancer may be diagnosed as cancerous. Though classifier models910, 920, and 930 share prognostic parameters between models, the sharedprognostic parameters shown are an example of one possible embodiment,and the exemplary prognostic parameters in each classifier model couldbe of any acoustic data type, such as those disclosed herein.

Example 1

Clinical studies were conducted to develop a method of generating anenhanced image of a volume of tissue and a method for characterizing avolume of breast tissue of a patient. Results are presented fromclinical studies that utilize breast imaging that is based on ultrasoundtomography, which quantifies tissue characteristics while also producing3-D images of breast anatomy, which methods described herein mayinclude.

Informed consent was obtained from all patients, prospectively recruitedin an IRB-approved protocol following HIPAA guidelines. Coronal imageswere produced by tomographic algorithms for reflection, sound speed andattenuation. All images were reviewed by a board-certified radiologistwith more than 20 years of experience in breast imaging andUS-technology development. In the first phase of the study, UST imageswere compared to multi-modal imaging to determine the appearance oflesions and breast parenchyma. In the second phase of the study,correlative comparisons with magnetic resonance (MR) breast imaging wereused to establish basic operational capabilities of the ultrasoundtomography (UST) system including the identification andcharacterization of parenchymal patterns, determination of the spatialresolution of UST and an estimate the breast volume that can imaged withUST. The third phase of the study focused on lesion characterization.Region of interest (ROI) analysis was performed on all identifiedlesions using all three UST image types. Combinations of the ROIgenerated values were used to characterize all masses in the study.

The studies demonstrated a high degree of correlation of breast tissuestructures relative to fat subtracted contrast enhanced MRI and theability to scan ˜90% of the volume of the breast at a resolution of 0.7mm in the coronal plane. With a scan duration of ˜1-3 minutes, nosignificant motion artifacts were observed. Initial clinical resultssuggest an ability to characterize lesions using margin boundary scoresin combination with sound speed and attenuation parameters.

UST and MR imaging was performed within weeks of each other. UST imagingwas carried out with the SoftVue system (Delphinus Medical Technologies)and the MR exams with a Philips Achieva 3T system. The resulting imagesequences were qualitatively and quantitatively to assess imagingperformance of UST. As discussed above, UST images correlate best withMR images. Further inspection shows that of the three UST image types,the sound speed image correlates best with MR. FIG. 10A shows a coronalview comparison between UST speed of sound and MR contrast enhanced fatsubtracted images of representative breast parenchyma.

The parenchymal patterns are very similar with the only major differencerelating to the shape of the breast. This difference can be explained bythe fact that the SoftVue system utilizes water so that buoyancyforeshortens the breast while with MR, gravity lengthens the breast inthe AP dimension (e.g., prone). As discussed above, UST images correlatebest with MR images. Further inspection shows that of the three USTimage types, the sound speed image correlates best with MR, asillustrated in FIG. 10A.

MRI and UST breast volumes were compared using a paired t-test. In thefirst step, a k-means segmentation algorithm was applied to Ti breast MRimages to automatically separate out the non-tissue background. In thesecond step, the boundary between the breast tissue and the chest wallwas drawn manually and the chest wall removed, leaving behind onlybreast tissue.

In the UST images a semi-automated tool was used to draw a boundaryaround the breast tissue in each coronal slice and everything outsidethe boundary removed (water signal). Any slices containing chest wallsignal were also removed.

The spatial resolution of each modality was estimated using profile cutsof thin features, using the full-width, half-maximum criterion as shownin FIG. 10B. The results of the spatial resolution analysis are shown inthe table below. The spatial resolution was found to be dependent on thereprojection type for both Mill and with UST outperforming MRI in thecoronal plane and Mill outperforming UST in the other projections.(However, MR acquisitions with isotropic voxels would show comparableresolution to UST in the coronal plane). The UST image voxels are notisotropic and data acquisition cannot be readily adjusted like MR, suchthat UST reconstructed in axial and sagittal planes have resolution thatapproach the 2.5-mm slice thickness at this time.

Resolution UST MRI Coronal 0.7 +/− 0.1 mm 1.6 +/− 0.3 mm Axial/Sagittal2.5 +/− 0.5 mm 0.8 +/− 0.1 mm

US-BI-RADS criteria are predominantly devoted to assessment of tumorshape, margins, and interaction with adjacent tissue. However, criteriasuch as shadowing or enhanced through transmission are not applicable toUST's circular geometry. In addition, UST, operating at 3 MHz, appearsmore sensitive to the specular reflectors of benign mass capsules, orthe spiculations and/or architectural distortions of many cancers.Therefore, we developed a 5-point scale (the margin boundary score) thatcombined US-BI-RADS criteria for tumor margins, as well as possibilitiesfor peritumoral tissue interaction.

An ultimate goal may be to generate textural analyses that may be lessoperator dependent and serve as appropriate diagnostic aids for adetected mass by simply requiring the radiologist to draw an ellipsoidalROI. FIG. 11 shows the basic differences in sound speed texture andmorphology noted for many cysts, fibroadenomas, and cancer. Based on themargin boundary score's five point scale, a classifier model can beimplemented using classifier model 910, wherein a margin boundary scoreequal to or above a threshold value of three may be diagnosed as cancer.A first table showing the type of lesion, the assessed margin boundaryscore of the region for the patients surveyed (containing 107 benignlesions and 31 cancers) is shown below:

SS Margin Boundary Cancer Fibro Cyst Benign 1 2 32 26 2 2 1 16 11 8 3 65 3 3 4 19 0 0 1 5 3 0 0 0 Total 31 53 40 41

A second summary table showing the diagnostic results is shown below:

Sensitivity  90% Total Positive 28 Specificity 88.8% False Positive 12PPV  70% Total Negative 95 NPV 96.9% False Negative 3 Accuracy 90.3%Total 138

Additionally, masses were characterized by a (i) Margin Boundary score,(ii) reflectivity, (iii) quantitative SS evaluation, and (iv) ATTevaluations. A semi-automatic Region-of-interest (ROI) tool was used todetermine the quantitative properties of each mass. After identifyingthe mass of interest, a simple elliptical ROI may be drawn around themass. The ROI algorithm then generates 20 radial ellipsoids˜10 insideand 10 outside the mass. Quantitative information was then measured foreach of the 20 annuli for subsequent analysis. The region of interest(ROI) analysis was performed on all identified lesions using all threeUST image types. Combinations of the ROI generated values were used tocharacterize all masses in the study.

Our recent data highlights the significant impacts of first-orderstatistics, such as standard deviation, within the tumoral ROI andcomparisons with the surrounding peritumoral region. FIGS. 12A-12C showbox plots summarizing said first order statistics including: the meanenhanced reflection (ErfMean), the relative mean enhanced reflectioninterior and exterior to the ROI (ErfRelDist), the standard deviation ofthe enhanced reflection (Erf_SD), the mean sound speed (SSMean), therelative mean sound speed interior and exterior to the ROI (SSRelDist),the standard deviation of the sound speed (SS_SD), the mean attenuation(AtMean), the standard deviation of the attenuation (At_SD), and thestandard deviation of the attenuation corrected for the margin boundaryscore (Corr_At_SD). Each box plot also contains a summary table showingthe associated value of the statistic for various types of lesions. Thebox plots were based on taking the average values for 107 benign lesionsand 31 cancers.

Scatterplots and box plots of the optimal methods were used toillustrate the characterization potential. The box plot in FIG. 12Cshows the differentiation achieved when using the boundary scorecombined with the first-order statistic of standard deviation, a morecrude measure of heterogeneity, based upon tumoral ROI extracted fromATT images, which had only slightly higher significance than SS. TheseROIs were again obtained by simply drawing an elliptical ROI around themass and determining the standard deviation with in the ROI.

Upon further investigation, it was found that the SS of the peritumoralmass region (defined by an annular area just outside the mass boundaryROI) further separated the benign masses from cancer. The following datais generated using classifier method 920. A scatter plot based on all ofthese parameters is shown in FIG. 13A. The scatter plot shows separatelythe cancers, fibroadenomas and cancers. The cancers are tightly groupedin the top left corner of the plot indicating high boundary scores, highheterogeneity (standard deviation of sound attenutation ≥0.0347) andlower peritumoral sound speed (mean sound speed in the peritumoralregion ≤1.51). By these measures, there was not much separation betweencysts and fibroadenomas but significant separation between them andcancer. ROC analysis of the data represented in the scatter plotindicates a PPV of 91% when the sensitivity is 97%. A summary tableshowing the results is shown below:

Sensitivity  97% Total Positive 30 Specificity 94.4% False Positive 6PPV  83% Total Negative 101 NPV 99.0% False Negative 1 Accuracy 96.8%Total 138

FIG. 13B shows a scatter plot based on the standard-deviation of thesound speed within the tumor and the standard-deviation of theattenuation within the tumor. An illustrative example of classifiermodel 920 using a linear function of the two prognostic parameters isshown by the line drawn through the scatter plot. If patient values foreach of the two prognostic parameters plotted in FIG. 13B are above andright of the line, the lesion is diagnosed as cancerous.

This is a subset of data relative to an expanded ongoing study thatincludes more quantitative margin analyses. This method can also serveas a teaching tool for identifying grossly apparent textural differenceswithin the tumor and surrounding peritumoral region.

Ultrasound Tomography System

FIGS. 2A-C show a schematic of an exemplary ultrasound scanner, aschematic of a patient breast in an exemplary ultrasound scanner, and aschematic of an exemplary ultrasound transducer of an ultrasoundscanner, respectively, in accordance with embodiments. As shown in FIGS.2A-C, an ultrasound tomography scanner 200 may comprise a transducer 220configured to receive the volume of tissue and comprising an array ofultrasound transmitters 224 and an array of ultrasound receivers 226.The array of ultrasound transmitters may be configured to emit acousticwaveforms toward the volume of tissue, and the array of ultrasoundreceivers 226 may be configured to detect a set of acoustic signalsderived from acoustic waveforms transmitted through the volume of tissueThe ultrasound tomography scanner 200 may further comprise a computer210 in communication with the transducer, comprising one or moreprocessors and non-transitory computer-readable media with instructionsstored thereon that when executed may be configured to perform themethods of generating an enhanced image of a volume of tissue, themethods of characterizing a volume of breast tissue, and embodiments andvariations described herein. The ultrasound tomography scanner 200 mayfurther comprise a display 290 in communication with the digitalprocessing device 210 and configured to render the enhanced image of thevolume of tissue.

The system 200 functions to render ultrasound images and/or generatetransformed ultrasound data that may be used to generate a highresolution image of structures present within a volume of tissue. Insome embodiments, the system 200 may function to produce images that maybe aligned with regulatory standards for medical imaging, as regulated,for instance, by the U.S. Food and Drug Administration (FDA). The system200 may be configured to implement at least a portion of an embodiment,variation, or example of method 100 described above; however, the system200 may additionally or alternatively be configured to implement anyother suitable method.

The transducer 220, the computer processor 210, and the display 290 maybe coupled to a scanner table 205, as shown in FIGS. 2A and 2B, whereinthe scanner table 205 has an opening 206 that provides access to thevolume of tissue of the patient. The table, which may be made of adurable, flexible material (e.g., flexible membrane, fabric, etc.), maycontour to the patient's body, thereby increasing scanning access to theaxilla regions of the breast and increasing patient comfort. The opening206 in the table may allow the breast (or other appendage) to protrudethrough the table and be submerged in an imaging tank 230 filled withwater or another suitable fluid as an acoustic coupling medium thatpropagates acoustic waves.

FIGS. 2B and 2C show a schematic of a patient breast in an exemplaryultrasound scanner and a schematic of an exemplary ultrasound transducerof an ultrasound scanner, in accordance with embodiments. As shown inFIGS. 2B and 2C, a ring-shaped transducer 220 with transducer elements222 may be located within the imaging tank 230 and encircle or otherwisesurround the breast, wherein each of the transducer elements 222 maycomprise one of the array of ultrasound transmitters 224 paired with oneof the array of ultrasound receivers 226. Multiple ultrasoundtransmitters 224 that direct safe, non-ionizing ultrasound pulses towardthe tissue and multiple ultrasound receivers 226 that receive and recordacoustic signals scattering from the tissue and/or transmitted throughthe tissue may be distributed around the ring transducer 220. In oneembodiment, transducer 220 may be organized such that each ultrasoundtransmitter element may be paired with a corresponding ultrasoundreceiver element, each ultrasound transmitter element may be surroundedby two adjacent ultrasound transmitter elements, each ultrasoundreceiver element may be surrounded by two adjacent ultrasound receiverelements, and the transducer may be axially symmetric, as in FIG. 2C.

During the scan, the ring transducer 220 may pass along the tissue, suchas in an anterior-posterior direction between the chest wall and thenipple region of the breast to acquire an acoustic data set includingmeasurements such as acoustic reflection, acoustic attenuation, andsound speed. The data set may be acquired at discrete scanning steps, orcoronal “slices”. The transducer 220 may be configured to scan step-wisein increments from the chest wall towards the nipple, and/or from thenipple towards the chest wall. However, the transducer 220 mayadditionally and/or alternatively receive data regarding any suitablebiomechanical property of the tissue during the scan, and in anysuitable direction.

In some embodiments, the scanner table may comprise an embodiment,variation, or example of the patient interface system described in anyof the references incorporated herein. However, system 200 mayadditionally or alternatively comprise or be coupled with any othersuitable patient interface system.

Digital Processing Device

In some embodiments, the platforms, systems, media, and methodsdescribed herein include a digital processing device, or use of thesame. In further embodiments, the digital processing device includes oneor more hardware central processing units (CPUs), general purposegraphics processing units (GPGPUs), or field programmable gate arrays(FPGAs) that carry out the device's functions. In still furtherembodiments, the digital processing device further comprises anoperating system configured to perform executable instructions. In someembodiments, the digital processing device may be optionally connected acomputer network. In further embodiments, the digital processing deviceis optionally connected to the Internet such that it accesses the WorldWide Web. In still further embodiments, the digital processing device isoptionally connected to a cloud computing infrastructure. In otherembodiments, the digital processing device is optionally connected to anintranet. In other embodiments, the digital processing device isoptionally connected to a data storage device.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers, mediastreaming devices, handheld computers, Internet appliances, mobilesmartphones, tablet computers, personal digital assistants, video gameconsoles, and vehicles. Those of skill in the art will recognize thatmany smartphones are suitable for use in the system described herein.Those of skill in the art will also recognize that select televisions,video players, and digital music players with optional computer networkconnectivity are suitable for use in the system described herein.Suitable tablet computers include those with booklet, slate, andconvertible configurations, known to those of skill in the art.

In some embodiments, the digital processing device includes an operatingsystem configured to perform executable instructions. The operatingsystem is, for example, software, including programs and data, whichmanages the device's hardware and provides services for execution ofapplications. Those of skill in the art will recognize that suitableserver operating systems include, by way of non-limiting examples,FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle®Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in theart will recognize that suitable personal computer operating systemsinclude, by way of non-limiting examples, Microsoft® Windows®, Apple®Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. Insome embodiments, the operating system is provided by cloud computing.Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia®Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google®Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS,Linux °, and Palm® WebOS®. Those of skill in the art will also recognizethat suitable media streaming device operating systems include, by wayof non-limiting examples, Apple TV®, Roku®, Boxee®, Google TV®, GoogleChromecast®, Amazon Fire®, and Samsung® HomeSync®. Those of skill in theart will also recognize that suitable video game console operatingsystems include, by way of non-limiting examples, Sony® PS3°, Sony®PS4®, Microsoft® Xbox 360®, Microsoft Xbox One, Nintendo® Wii®,Nintendo® Wii U®, and Ouya®.

In some embodiments, the device includes a storage and/or memory device.The storage and/or memory device is one or more physical apparatusesused to store data or programs on a temporary or permanent basis. Insome embodiments, the device is volatile memory and requires power tomaintain stored information. In some embodiments, the device isnon-volatile memory and retains stored information when the digitalprocessing device is not powered. In further embodiments, thenon-volatile memory comprises flash memory. In some embodiments, thenon-volatile memory comprises dynamic random-access memory (DRAM). Insome embodiments, the non-volatile memory comprises ferroelectric randomaccess memory (FRAM). In some embodiments, the non-volatile memorycomprises phase-change random access memory (PRAM). In otherembodiments, the device is a storage device including, by way ofnon-limiting examples, CD-ROMs, DVDs, flash memory devices, magneticdisk drives, magnetic tapes drives, optical disk drives, and cloudcomputing based storage. In further embodiments, the storage and/ormemory device is a combination of devices such as those disclosedherein.

In some embodiments, the digital processing device includes a display tosend visual information to a user. In some embodiments, the display is acathode ray tube (CRT). In some embodiments, the display is a liquidcrystal display (LCD). In further embodiments, the display is a thinfilm transistor liquid crystal display (TFT-LCD). In some embodiments,the display is an organic light emitting diode (OLED) display. Invarious further embodiments, on OLED display is a passive-matrix OLED(PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments,the display is a plasma display. In other embodiments, the display is avideo projector. In still further embodiments, the display is acombination of devices such as those disclosed herein.

In some embodiments, the digital processing device includes an inputdevice to receive information from a user. In some embodiments, theinput device is a keyboard. In some embodiments, the input device is apointing device including, by way of non-limiting examples, a mouse,trackball, track pad, joystick, game controller, or stylus. In someembodiments, the input device is a touch screen or a multi-touch screen.In other embodiments, the input device is a microphone to capture voiceor other sound input. In other embodiments, the input device is a videocamera or other sensor to capture motion or visual input. In furtherembodiments, the input device is a Kinect, Leap Motion, or the like. Instill further embodiments, the input device is a combination of devicessuch as those disclosed herein.

Referring to FIG. 14, in a particular embodiment, an exemplary digitalprocessing device 210 is programmed with instructions or otherwiseconfigured to implement the method of generating an enhanced image of avolume of tissue and the method for characterizing a volume of breasttissue of a patient, as described herein. The device 210 may regulatevarious aspects of the ultrasound tomography system, imaging methods,and characterizing methods of the present disclosure, such as, forexample, performing processing steps. In this embodiment, the digitalprocessing device 210 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1405, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The digital processing device 210 also includes memory ormemory location 1410 (e.g., random-access memory, read-only memory,flash memory), electronic storage unit 1415 (e.g., hard disk),communication interface 1420 (e.g., network adapter) for communicatingwith one or more other systems, and peripheral devices 1425, such ascache, other memory, data storage and/or electronic display adapters.The memory 1410, storage unit 1415, interface 1420 and peripheraldevices 1425 are in communication with the CPU 1405 through acommunication bus (solid lines), such as a motherboard. The storage unit1415 can be a data storage unit (or data repository) for storing data.The digital processing device 210 can be operatively coupled to acomputer network (“network”) 1430 with the aid of the communicationinterface 1420. The network 1430 can be the Internet, an internet and/orextranet, or an intranet and/or extranet that is in communication withthe Internet. The network 1430 in some cases is a telecommunicationand/or data network. The network 1430 can include one or more computerservers, which can enable distributed computing, such as cloudcomputing. The network 1430, in some cases with the aid of the device210, can implement a peer-to-peer network, which may enable devicescoupled to the device 210 to behave as a client or a server.

Continuing to refer to FIG. 14, the CPU 1405 can execute a sequence ofmachine-readable instructions, which can be embodied in a program orsoftware. The instructions may be stored in a memory location, such asthe memory 1410. The instructions can be directed to the CPU 1405, whichcan subsequently program or otherwise configure the CPU 1405 toimplement methods of the present disclosure. Examples of operationsperformed by the CPU 1405 can include fetch, decode, execute, and writeback. The CPU 1405 can be part of a circuit, such as an integratedcircuit. One or more other components of the device 210 can be includedin the circuit. In some cases, the circuit is an application specificintegrated circuit (ASIC) or a field programmable gate array (FPGA).

Continuing to refer to FIG. 14, the storage unit 1415 can store files,such as drivers, libraries and saved programs. The storage unit 1415 canstore user data, e.g., user preferences and user programs. The digitalprocessing device 210 in some cases can include one or more additionaldata storage units that are external, such as located on a remote serverthat is in communication through an intranet or the Internet.

Continuing to refer to FIG. 14, the digital processing device 210 cancommunicate with one or more remote computer systems through the network1430. For instance, the device 210 can communicate with a remotecomputer system of a user. Examples of remote computer systems includepersonal computers (e.g., portable PC), slate or tablet PCs (e.g.,Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g.,Apple® iPhone, Android-enabled device, Blackberry®), or personal digitalassistants.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the digital processing device 210, such as, for example, onthe memory 1410 or electronic storage unit 1415. The machine executableor machine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1405. In some cases, thecode can be retrieved from the storage unit 1415 and stored on thememory 1410 for ready access by the processor 1405. In some situations,the electronic storage unit 1415 can be precluded, andmachine-executable instructions are stored on memory 1410.

The digital processing device 210 can include or be in communicationwith an electronic display 290 that comprises a user interface (UI)1440. Examples of UI's include, without limitation, a graphical userinterface (GUI) and web-based user interface. In some cases, electronicdisplay 290 may be connected to the computer system 210 via a network,e.g., via network 1430.

Non-Transitory Computer Readable Storage Medium

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more non-transitory computer readablestorage media encoded with a program including instructions executableby the operating system of an optionally networked digital processingdevice. In further embodiments, a computer readable storage medium is atangible component of a digital processing device. In still furtherembodiments, a computer readable storage medium is optionally removablefrom a digital processing device. In some embodiments, a computerreadable storage medium includes, by way of non-limiting examples,CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic diskdrives, magnetic tape drives, optical disk drives, cloud computingsystems and services, and the like. In some cases, the program andinstructions are permanently, substantially permanently,semi-permanently, or non-transitorily encoded on the media.

Computer Program

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include at least one computer program, or use of thesame. A computer program includes a sequence of instructions, executablein the digital processing device's CPU, written to perform a specifiedtask. Computer readable instructions may be implemented as programmodules, such as functions, objects, Application Programming Interfaces(APIs), data structures, and the like, that perform particular tasks orimplement particular abstract data types. In light of the disclosureprovided herein, those of skill in the art will recognize that acomputer program may be written in various versions of variouslanguages.

The functionality of the computer readable instructions may be combinedor distributed as desired in various environments. In some embodiments,a computer program comprises one sequence of instructions. In someembodiments, a computer program comprises a plurality of sequences ofinstructions. In some embodiments, a computer program is provided fromone location. In other embodiments, a computer program is provided froma plurality of locations. In various embodiments, a computer programincludes one or more software modules. In various embodiments, acomputer program includes, in part or in whole, one or more webapplications, one or more mobile applications, one or more standaloneapplications, one or more web browser plug-ins, extensions, add-ins, oradd-ons, or combinations thereof

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Objective-C,COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET,or combinations thereof. Compilation is often performed, at least inpart, to create an executable program. In some embodiments, a computerprogram includes one or more executable complied applications.

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include software, server, and/or database modules, oruse of the same. In view of the disclosure provided herein, softwaremodules are created by techniques known to those of skill in the artusing machines, software, and languages known to the art. The softwaremodules disclosed herein are implemented in a multitude of ways. Invarious embodiments, a software module comprises a file, a section ofcode, a programming object, a programming structure, or combinationsthereof. In further various embodiments, a software module comprises aplurality of files, a plurality of sections of code, a plurality ofprogramming objects, a plurality of programming structures, orcombinations thereof. In various embodiments, the one or more softwaremodules comprise, by way of non-limiting examples, a web application, amobile application, and a standalone application. In some embodiments,software modules are in one computer program or application. In otherembodiments, software modules are in more than one computer program orapplication. In some embodiments, software modules are hosted on onemachine. In other embodiments, software modules are hosted on more thanone machine. In further embodiments, software modules are hosted oncloud computing platforms. In some embodiments, software modules arehosted on one or more machines in one location. In other embodiments,software modules are hosted on one or more machines in more than onelocation.

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more databases, or use of the same. Inview of the disclosure provided herein, those of skill in the art willrecognize that many databases are suitable for storage and retrieval ofinformation. In various embodiments, suitable databases include, by wayof non-limiting examples, relational databases, non-relationaldatabases, object oriented databases, object databases,entity-relationship model databases, associative databases, and XMLdatabases. Further non-limiting examples include SQL, PostgreSQL, MySQL,Oracle, DB2, and Sybase. In some embodiments, a database isinternet-based. In further embodiments, a database is web-based. Instill further embodiments, a database is cloud computing-based. In otherembodiments, a database is based on one or more local computer storagedevices

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

1.-30. (canceled)
 31. A method for characterizing a volume of breasttissue of a patient, the method comprising: receiving from a transducer,a plurality of acoustic signals derived from acoustic waveformstransmitted through the volume of tissue, wherein the transducercomprises an array of ultrasound transmitters and an array of ultrasoundreceivers configured to surround the volume of tissue; generating fromthe plurality of acoustic signals, a three-dimensional acousticrendering that characterizes sound propagation within the volume oftissue; receiving an input from a user corresponding to a user-selectedregion of interest; generating from the acoustic rendering a firstplurality of prognostic parameters corresponding to sound propagationinterior to the user-selected region of interest; generating from theacoustic rendering a second plurality of prognostic parameterscorresponding to sound propagation exterior to the user-selected regionof interest; and characterizing a lesion within the volume of tissueusing the first plurality of interior prognostic parameters and thesecond plurality of exterior prognostic parameters.
 32. The method ofclaim 31, wherein the three-dimensional acoustic rendering comprises anacoustic attenuation rendering, an acoustic reflection rendering, and anacoustic sound speed rendering.
 33. The method of claim 32, furthercomprising generating a merged three-dimensional rendering of theacoustic reflection rendering, the acoustic attenuation rendering, andthe acoustic sound speed rendering.
 34. The method of claim 32, whereinthe first plurality of prognostic parameters comprises a volume-averagevalue of acoustic attenuation, a volume-average value of acoustic speed,and a volume-average value of acoustic reflectivity.
 35. The method ofclaim 32, wherein the second plurality of prognostic parameterscomprises a volume-average value of acoustic attenuation, avolume-average value of acoustic speed, and a volume-average value ofacoustic reflectivity.
 36. The method of claim 32, wherein theuser-selected region of interest is selected from at least one of anacoustic attenuation rendering, an acoustic reflection rendering, and anacoustic sound speed rendering.
 37. The method of claim 33, wherein theuser-selected region of interest is selected from the merged threedimensional rendering.
 38. The method of claim 31, wherein selection ofthe user-selected region of interest is aided or optimized by acomputer-implemented algorithm.
 39. The method of claim 31, wherein alesion in the volume of tissue is classified as at least one of acancerous tumor, a fibroadenoma, a cyst, a nonspecific benign mass, andan unidentifiable mass.
 40. The method of claim 31, wherein classifyinga lesion in the volume of tissue comprises using a threshold value ofthe first plurality of prognostic parameters or the second plurality ofprognostic parameters.
 41. The method of claim 31, wherein at least oneprognostic parameter among the first plurality of prognostic parametersor the second plurality of prognostic parameters comprises auser-selected classification of a margin of the region of interestaccording to an integer scale.
 42. The method of claim 31, wherein atleast one prognostic parameter among the first plurality of prognosticparameters or the second plurality of prognostic parameters comprises adifference between an acoustic parameter interior to the region ofinterest and an acoustic parameter exterior to the region of interest.43. The method of claim 32, wherein the first plurality of prognosticparameters comprises a volume-standard-deviation value of acousticattenuation, a volume-average value of acoustic speed, and avolume-average value of acoustic reflectivity.
 44. The method of claim32, wherein the first plurality of prognostic parameters comprises avolume-standard-deviation value of acoustic attenuation, avolume-average value of acoustic speed, and a volume-average value ofacoustic reflectivity.
 45. The method of claim 39, wherein thespecificity of classification of a lesion in the volume of tissue isincreased such that the specificity is at least about 75% to at leastabout 95%.
 46. A system for generating enhanced images of a volume oftissue, the system comprising: a transducer array comprising an array ofultrasound emitters and an array of ultrasound receivers, the transducerarray configured to surround a volume of tissue, wherein the array ofultrasound transmitters is configured to emit acoustic waveforms towardthe volume of tissue, wherein the array of ultrasound receivers isconfigured to receive the emitted acoustic waveforms and convert thereceived acoustic waveforms to a plurality of acoustic signals; aprocessor comprising a computer readable medium configured withinstructions, that when executed, cause the processor to: receive fromthe transducer, the plurality of acoustic signals; generate from theplurality of acoustic signals, a three-dimensional acoustic renderingthat characterizes sound propagation within the volume of tissue;receive an input from a user corresponding to a user-selected region ofinterest; generate from the acoustic rendering a first plurality ofprognostic parameters corresponding to sound propagation interior to theuser-selected region of interest; generate from the acoustic rendering asecond plurality of prognostic parameters corresponding to soundpropagation exterior to the user-selected region of interest; andcharacterize a lesion within the volume of tissue using the firstplurality of interior prognostic parameters and the second plurality ofexterior prognostic parameters; and a display visible to a userconfigured to display the one or more combined images.
 47. A method ofbreast ultrasound imaging and analysis, comprising: scanning a breastwith ultrasound tomography; determining a tumor sound speed and soundreflection based on the scanning; calculating a gradient of the tumorsound speed; determining a tumor sound reflectivity based on thegradient and a response to a radio frequency component of the soundreflection; and outputting an image of the breast based on the tumorsound reflectivity.
 48. A method of breast ultrasound imaging andanalysis, comprising: scanning a breast with ultrasound tomography;determining a tumor sound speed, sound reflection, and sound attenuationbased on the scanning; generating a reflection score based on the tumorsound speed and attenuation; characterizing mass margins of the breastbased on the reflection score; and identifying a tumoral or peritumoralregion of interest within the breast.